Category: Data Analysis

AI, AI Strategy, Analytics, Artificial Intelligence (AI), Computer Vision, Data Analysis, Data Careers, Data Education & Training, Data Governance, Data News, Data Science, Data Strategy, Deep Learning, Generative AI, Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics

AI in the Energy Industry: Powering Reliability, Efficiency, and the Energy Transition
“AI in …” series

The energy industry sits at the crossroads of reliability, cost pressure, regulation, and decarbonization. Whether it’s oil and gas, utilities, renewables, or grid operators, energy companies manage massive physical assets and generate oceans of operational data. AI has become a critical tool for turning that data into faster decisions, safer operations, and more resilient energy systems.

From predicting equipment failures to balancing renewable power on the grid, AI is increasingly embedded in how energy is produced, distributed, and consumed.

How AI Is Being Used in the Energy Industry Today

Predictive Maintenance & Asset Reliability

  • Shell uses machine learning to predict failures in rotating equipment across refineries and offshore platforms, reducing downtime and safety incidents.
  • BP applies AI to monitor pumps, compressors, and drilling equipment in real time.

Grid Optimization & Demand Forecasting

  • National Grid uses AI-driven forecasting to balance electricity supply and demand, especially as renewable energy introduces more variability.
  • Utilities apply AI to predict peak demand and optimize load balancing.

Renewable Energy Forecasting

  • Google DeepMind has worked with wind energy operators to improve wind power forecasts, increasing the value of wind energy sold to the grid.
  • Solar operators use AI to forecast generation based on weather patterns and historical output.

Exploration & Production (Oil and Gas)

  • ExxonMobil uses AI and advanced analytics to interpret seismic data, improving subsurface modeling and drilling accuracy.
  • AI helps optimize well placement and drilling parameters.

Energy Trading & Price Forecasting

  • AI models analyze market data, weather, and geopolitical signals to optimize trading strategies in electricity, gas, and commodities markets.

Customer Engagement & Smart Metering

  • Utilities use AI to analyze smart meter data, detect outages, identify energy theft, and personalize energy efficiency recommendations for customers.

Tools, Technologies, and Forms of AI in Use

Energy companies typically rely on a hybrid of industrial, analytical, and cloud technologies:

  • Machine Learning & Deep Learning
    Used for forecasting, anomaly detection, predictive maintenance, and optimization.
  • Time-Series Analytics
    Critical for analyzing sensor data from turbines, pipelines, substations, and meters.
  • Computer Vision
    Used for inspecting pipelines, wind turbines, and transmission lines via drones.
    • GE Vernova applies AI-powered inspection for turbines and grid assets.
  • Digital Twins
    Virtual replicas of power plants, grids, or wells used to simulate scenarios and optimize performance.
    • Siemens Energy and GE Digital offer digital twin platforms widely used in the industry.
  • AI & Energy Platforms
    • GE Digital APM (Asset Performance Management)
    • Siemens Energy Omnivise
    • Schneider Electric EcoStruxure
    • Cloud platforms such as Azure Energy, AWS for Energy, and Google Cloud for scalable AI workloads
  • Edge AI & IIoT
    AI models deployed close to physical assets for low-latency decision-making in remote environments.

Benefits Energy Companies Are Realizing

Energy companies using AI effectively report significant gains:

  • Reduced Unplanned Downtime and maintenance costs
  • Improved Safety through early detection of hazardous conditions
  • Higher Asset Utilization and longer equipment life
  • More Accurate Forecasts for demand, generation, and pricing
  • Better Integration of Renewables into existing grids
  • Lower Emissions and Energy Waste

In an industry where assets can cost billions, small improvements in uptime or efficiency have outsized impact.

Pitfalls and Challenges

Despite its promise, AI adoption in energy comes with challenges:

Data Quality and Legacy Infrastructure

  • Older assets often lack sensors or produce inconsistent data, limiting AI effectiveness.

Integration Across IT and OT

  • Connecting enterprise systems with operational technology remains complex and risky.

Model Trust and Explainability

  • Operators must trust AI recommendations—especially when safety or grid stability is involved.

Cybersecurity Risks

  • Increased connectivity and AI-driven automation expand the attack surface.

Overambitious Digital Programs

  • Some AI initiatives fail because they aim for full digital transformation without clear, phased business value.

Where AI Is Headed in the Energy Industry

The next phase of AI in energy is tightly linked to the energy transition:

  • AI-Driven Grid Autonomy
    Self-healing grids that detect faults and reroute power automatically.
  • Advanced Renewable Optimization
    AI coordinating wind, solar, storage, and demand response in real time.
  • AI for Decarbonization & ESG
    Optimization of emissions tracking, carbon capture systems, and energy efficiency.
  • Generative AI for Engineering and Operations
    AI copilots generating maintenance procedures, engineering documentation, and regulatory reports.
  • End-to-End Energy System Digital Twins
    Modeling entire grids or energy ecosystems rather than individual assets.

How Energy Companies Can Gain an Advantage

To compete and innovate effectively, energy companies should:

  1. Prioritize High-Impact Operational Use Cases
    Predictive maintenance, grid optimization, and forecasting often deliver the fastest ROI.
  2. Modernize Data and Sensor Infrastructure
    AI is only as good as the data feeding it.
  3. Design for Reliability and Explainability
    Especially critical for safety- and mission-critical systems.
  4. Adopt a Phased, Asset-by-Asset Approach
    Scale proven solutions rather than pursuing sweeping transformations.
  5. Invest in Workforce Upskilling
    Engineers and operators who understand AI amplify its value.
  6. Embed AI into Sustainability Strategy
    Use AI not just for efficiency, but for measurable decarbonization outcomes.

Final Thoughts

AI is rapidly becoming foundational to the future of energy. As the industry balances reliability, affordability, and sustainability, AI provides the intelligence needed to operate increasingly complex systems at scale.

In energy, AI isn’t just optimizing machines—it’s helping power the transition to a smarter, cleaner, and more resilient energy future.

Data Analysis, Data Cleaning, Data Development, Data Munging, Data Quality Assurance, Data Wrangling, Power BI, Power Query

How to Perform a Safe DIVIDE in Power BI (DAX and Power Query)

Division is a common operation in Power BI, but it can cause errors when the divisor is zero. Both DAX and Power Query provide built-in ways to handle these scenarios safely.

Safe DIVIDE in DAX

In DAX, the DIVIDE function is the recommended approach. Its syntax is:

DIVIDE(numerator, divisor [, alternateResult])

If the divisor is zero (or BLANK), the function returns the optional alternateResult; otherwise, it performs the division normally.

Examples:

  • DIVIDE(10, 2) → 5
  • DIVIDE(10, 0) → BLANK
  • DIVIDE(10, 0, 0) → 0

This makes DIVIDE safer and cleaner than using conditional logic.

Safe DIVIDE in Power Query

In Power Query (M language), you can use the try … otherwise expression to handle divide-by-zero errors gracefully. The syntax is:

try [expression] otherwise [alternateValue]

Example:

try [Sales] / [Quantity] otherwise 0

If the division fails (such as when Quantity is zero), Power Query returns 0 instead of an error.

Using DIVIDE in DAX and try … otherwise in Power Query ensures your division calculations remain error-free.

AI, AI Governance, AI Strategy, Analytics, Artificial Intelligence (AI), Cloud computing, Computer Vision, Data Analysis, Data Careers, Data Education & Training, Data Governance, Data Integration, Data Strategy, Deep Learning, Generative AI, HR analytics, Large Language Models (LLMs), Machine Learning (ML), Natural Language Processing (NLP), People Analytics, Predictive Analytics, Workday, Workforce Analytics

AI in Human Resources: From Administrative Support to Strategic Workforce Intelligence
“AI in …” series

Human Resources has always been about people—but it’s also about data: skills, performance, engagement, compensation, and workforce planning. As organizations grow more complex and talent markets tighten, HR teams are being asked to move faster, be more predictive, and deliver better employee experiences at scale.

AI is increasingly the engine enabling that shift. From recruiting and onboarding to learning, engagement, and workforce planning, AI is transforming how HR operates and how employees experience work.

How AI Is Being Used in Human Resources Today

AI is now embedded across the end-to-end employee lifecycle:

Talent Acquisition & Recruiting

  • LinkedIn Talent Solutions uses AI to match candidates to roles based on skills, experience, and career intent.
  • Workday Recruiting and SAP SuccessFactors apply machine learning to rank candidates and surface best-fit applicants.
  • Paradox (Olivia) uses conversational AI to automate candidate screening, scheduling, and frontline hiring at scale.

Resume Screening & Skills Matching

  • Eightfold AI and HiredScore use deep learning to infer skills, reduce bias, and match candidates to open roles and future opportunities.
  • AI shifts recruiting from keyword matching to skills-based hiring.

Employee Onboarding & HR Service Delivery

  • ServiceNow HR Service Delivery uses AI chatbots to answer employee questions, guide onboarding, and route HR cases.
  • Microsoft Copilot for HR scenarios help managers draft job descriptions, onboarding plans, and performance feedback.

Learning & Development

  • Degreed and Cornerstone AI recommend personalized learning paths based on role, skills gaps, and career goals.
  • AI-driven content curation adapts as employee skills evolve.

Performance Management & Engagement

  • Betterworks and Lattice use AI to analyze feedback, goal progress, and engagement signals.
  • Sentiment analysis helps HR identify burnout risks or morale issues early.

Workforce Planning & Attrition Prediction

  • Visier applies AI to predict attrition risk, model workforce scenarios, and support strategic planning.
  • HR leaders use AI insights to proactively retain key talent.

Those are just a few examples of AI tools and scenarios in use. There are a lot more AI solutions for HR out there!

Tools, Technologies, and Forms of AI in Use

HR AI platforms combine people data with advanced analytics:

  • Machine Learning & Predictive Analytics
    Used for attrition prediction, candidate ranking, and workforce forecasting.
  • Natural Language Processing (NLP)
    Powers resume parsing, sentiment analysis, chatbots, and document generation.
  • Generative AI & Large Language Models (LLMs)
    Used to generate job descriptions, interview questions, learning content, and policy summaries.
    • Examples: Workday AI, Microsoft Copilot, Google Duet AI, ChatGPT for HR workflows
  • Skills Ontologies & Graph AI
    Used by platforms like Eightfold AI to map skills across roles and career paths.
  • HR AI Platforms
    • Workday AI
    • SAP SuccessFactors Joule
    • Oracle HCM AI
    • UKG Bryte AI

And there are AI tools being used across the entire employee lifecycle.

Benefits Organizations Are Realizing

Companies using AI effectively in HR are seeing meaningful benefits:

  • Faster Time-to-Hire and reduced recruiting costs
  • Improved Candidate and Employee Experience
  • More Objective, Skills-Based Decisions
  • Higher Retention through proactive interventions
  • Scalable HR Operations without proportional headcount growth
  • Better Strategic Workforce Planning

AI allows HR teams to spend less time on manual tasks and more time on high-impact, people-centered work.

Pitfalls and Challenges

AI in HR also carries significant risks if not implemented carefully:

Bias and Fairness Concerns

  • Poorly designed models can reinforce historical bias in hiring, promotion, or pay decisions.

Transparency and Explainability

  • Employees and regulators increasingly demand clarity on how AI-driven decisions are made.

Data Privacy and Trust

  • HR data is deeply personal; misuse or breaches can erode employee trust quickly.

Over-Automation

  • Excessive reliance on AI can make HR feel impersonal, especially in sensitive situations.

Failed AI Projects

  • Some initiatives fail because they focus on automation without aligning to HR strategy or culture.

Where AI Is Headed in Human Resources

The future of AI in HR is more strategic, personalized, and collaborative:

  • AI as an HR Copilot
    Assisting HR partners and managers with decisions, documentation, and insights in real time.
  • Skills-Centric Organizations
    AI continuously mapping skills supply and demand across the enterprise.
  • Personalized Employee Journeys
    Tailored learning, career paths, and engagement strategies.
  • Predictive Workforce Strategy
    AI modeling future talent needs based on business scenarios.
  • Responsible and Governed AI
    Stronger emphasis on ethics, explainability, and compliance.

How Companies Can Gain an Advantage with AI in HR

To use AI as a competitive advantage, organizations should:

  1. Start with High-Trust Use Cases
    Recruiting efficiency, learning recommendations, and HR service automation often deliver fast wins.
  2. Invest in Clean, Integrated People Data
    AI effectiveness depends on accurate and well-governed HR data.
  3. Design for Fairness and Transparency
    Bias testing and explainability should be built in from day one.
  4. Keep Humans in the Loop
    AI should inform decisions—not make them in isolation.
  5. Upskill HR Teams
    AI-literate HR professionals can better interpret insights and guide leaders.
  6. Align AI with Culture and Values
    Technology should reinforce—not undermine—the employee experience.

Final Thoughts

AI is reshaping Human Resources from a transactional function into a strategic engine for talent, culture, and growth. The organizations that succeed won’t be those that automate HR the most—but those that use AI to make work more human, more fair, and more aligned with business outcomes.

In HR, AI isn’t about replacing people—it’s about improving efficiency, elevating the candidate and employee experiences, and helping employees thrive.

AI, AI Strategy, Analytics, Artificial Intelligence (AI), Cloud computing, Computer Vision, Data Analysis, Data Careers, Data Education & Training, Data News, Data Science, Data Strategy, Data Visualization, Deep Learning, Generative AI, Large Language Models (LLMs), Machine Learning (ML), Natural Language Processing (NLP), Power BI, Power Query, Predictive Analytics, Python, SQL

AI Career Options for Early-Career Professionals and New Graduates

Artificial Intelligence is shaping nearly every industry, but breaking into AI right out of college can feel overwhelming. The good news is that you don’t need a PhD or years of experience to start a successful AI-related career. Many AI roles are designed specifically for early-career talent, blending technical skills with problem-solving, communication, and business understanding.

This article outlines excellent AI career options for people just entering the workforce, explaining what each role involves, why it’s a strong choice, and how to prepare with the right skills, tools, and learning resources.

1. AI / Machine Learning Engineer (Junior)

What It Is & What It Involves

Machine Learning Engineers build, train, test, and deploy machine learning models. Junior roles typically focus on:

  • Implementing existing models
  • Cleaning and preparing data
  • Running experiments
  • Supporting senior engineers

Why It’s a Good Option

  • High demand and strong salary growth
  • Clear career progression
  • Central role in AI development

Skills & Preparation Needed

Technical Skills

  • Python
  • SQL
  • Basic statistics & linear algebra
  • Machine learning fundamentals
  • Libraries: scikit-learn, TensorFlow, PyTorch

Where to Learn

  • Coursera (Andrew Ng ML specialization)
  • Fast.ai
  • Kaggle projects
  • University CS or data science coursework

Difficulty Level: ⭐⭐⭐⭐ (Moderate–High)

2. Data Analyst (AI-Enabled)

What It Is & What It Involves

Data Analysts use AI tools to analyze data, generate insights, and support decision-making. Tasks often include:

  • Data cleaning and visualization
  • Dashboard creation
  • Using AI tools to speed up analysis
  • Communicating insights to stakeholders

Why It’s a Good Option

  • Very accessible for new graduates
  • Excellent entry point into AI
  • Builds strong business and technical foundations

Skills & Preparation Needed

Technical Skills

  • SQL
  • Excel
  • Python (optional but helpful)
  • Power BI / Tableau
  • AI tools (ChatGPT, Copilot, AutoML)

Where to Learn

  • Microsoft Learn
  • Google Data Analytics Certificate
  • Kaggle datasets
  • Internships and entry-level analyst roles

Difficulty Level: ⭐⭐ (Low–Moderate)

3. Prompt Engineer / AI Specialist (Entry Level)

What It Is & What It Involves

Prompt Engineers design, test, and optimize instructions for AI systems to get reliable and accurate outputs. Entry-level roles focus on:

  • Writing prompts
  • Testing AI behavior
  • Improving outputs for business use cases
  • Supporting AI adoption across teams

Why It’s a Good Option

  • Low technical barrier
  • High demand across industries
  • Great for strong communicators and problem-solvers

Skills & Preparation Needed

Key Skills

  • Clear writing and communication
  • Understanding how LLMs work
  • Logical thinking
  • Domain knowledge (marketing, analytics, HR, etc.)

Where to Learn

  • OpenAI documentation
  • Prompt engineering guides
  • Hands-on practice with ChatGPT, Claude, Gemini
  • Real-world experimentation

Difficulty Level: ⭐⭐ (Low–Moderate)

4. AI Product Analyst / Associate Product Manager

What It Is & What It Involves

This role sits between business, engineering, and AI teams. Responsibilities include:

  • Defining AI features
  • Translating business needs into AI solutions
  • Analyzing product performance
  • Working with data and AI engineers

Why It’s a Good Option

  • Strong career growth
  • Less coding than engineering roles
  • Excellent mix of strategy and technology

Skills & Preparation Needed

Key Skills

  • Basic AI/ML concepts
  • Data analysis
  • Product thinking
  • Communication and stakeholder management

Where to Learn

  • Product management bootcamps
  • AI fundamentals courses
  • Internships or associate PM roles
  • Case studies and product simulations

Difficulty Level: ⭐⭐⭐ (Moderate)

5. AI Research Assistant / Junior Data Scientist

What It Is & What It Involves

These roles support AI research and experimentation, often in academic, healthcare, or enterprise environments. Tasks include:

  • Running experiments
  • Analyzing model performance
  • Data exploration
  • Writing reports and documentation

Why It’s a Good Option

  • Strong foundation for advanced AI careers
  • Exposure to real-world research
  • Great for analytical thinkers

Skills & Preparation Needed

Technical Skills

  • Python or R
  • Statistics and probability
  • Data visualization
  • ML basics

Where to Learn

  • University coursework
  • Research internships
  • Kaggle competitions
  • Online ML/statistics courses

Difficulty Level: ⭐⭐⭐⭐ (Moderate–High)

6. AI Operations (AIOps) / ML Operations (MLOps) Associate

What It Is & What It Involves

AIOps/MLOps professionals help deploy, monitor, and maintain AI systems. Entry-level work includes:

  • Model monitoring
  • Data pipeline support
  • Automation
  • Documentation

Why It’s a Good Option

  • Growing demand as AI systems scale
  • Strong alignment with data engineering
  • Less math-heavy than research roles

Skills & Preparation Needed

Technical Skills

  • Python
  • SQL
  • Cloud basics (Azure, AWS, GCP)
  • CI/CD concepts
  • ML lifecycle understanding

Where to Learn

  • Cloud provider learning paths
  • MLOps tutorials
  • GitHub projects
  • Entry-level data engineering roles

Difficulty Level: ⭐⭐⭐ (Moderate)

7. AI Consultant / AI Business Analyst (Entry Level)

What It Is & What It Involves

AI consultants help organizations understand and implement AI solutions. Entry-level roles focus on:

  • Use-case analysis
  • AI tool evaluation
  • Process improvement
  • Client communication

Why It’s a Good Option

  • Exposure to multiple industries
  • Strong soft-skill development
  • Fast career progression

Skills & Preparation Needed

Key Skills

  • Business analysis
  • AI fundamentals
  • Presentation and communication
  • Problem-solving

Where to Learn

  • Business analytics programs
  • AI fundamentals courses
  • Consulting internships
  • Case study practice

Difficulty Level: ⭐⭐⭐ (Moderate)

8. AI Content & Automation Specialist

What It Is & What It Involves

This role focuses on using AI to automate content, workflows, and internal processes. Tasks include:

  • Building automations
  • Creating AI-generated content
  • Managing tools like Zapier, Notion AI, Copilot

Why It’s a Good Option

  • Very accessible for non-technical graduates
  • High demand in marketing and operations
  • Rapid skill acquisition

Skills & Preparation Needed

Key Skills

  • Workflow automation
  • AI tools usage
  • Creativity and organization
  • Basic scripting (optional)

Where to Learn

  • Zapier and Make tutorials
  • Hands-on projects
  • YouTube and online courses
  • Real business use cases

Difficulty Level: ⭐⭐ (Low–Moderate)

How New Graduates Should Prepare for AI Careers

1. Build Foundations

  • Python or SQL
  • Data literacy
  • AI concepts (not just tools)

2. Practice with Real Projects

  • Personal projects
  • Internships
  • Freelance or volunteer work
  • Kaggle or GitHub portfolios

3. Learn AI Tools Early

  • ChatGPT, Copilot, Gemini
  • AutoML platforms
  • Visualization and automation tools

4. Focus on Communication

AI careers, and careers in general, reward those who can explain complex ideas simply.

Final Thoughts

AI careers are no longer limited to researchers or elite engineers. For early-career professionals, the best path is often a hybrid role that combines AI tools, data, and business understanding. Starting in these roles builds confidence, experience, and optionality—allowing you to grow into more specialized AI positions over time.
And the advice that many professionals give for gaining knowledge and breaking into the space is to “get your hands dirty”.

Good luck on your data journey!

Analytics, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Modeling, Data Wrangling, DP-600, Microsoft Certification, Microsoft Fabric, Power BI

Understanding the Power BI DAX “GENERATE / ROW” Pattern

The GENERATE / ROW pattern is an advanced but powerful DAX technique used to dynamically create rows and expand tables based on calculations. It is especially useful when you need to produce derived rows, combinations, or scenario-based expansions that don’t exist physically in your data model.

This article explains what the pattern is, when to use it, how it works, and provides practical examples. It assumes you are familiar with concepts such as row context, filter context, and iterators.

What Is the GENERATE / ROW Pattern?

At its core, the pattern combines two DAX functions:

  • GENERATE() – Iterates over a table and returns a union of tables generated for each row.
  • ROW() – Creates a single-row table with named columns and expressions.

Together, they allow you to:

  • Loop over an outer table
  • Generate one or more rows per input row
  • Shape those rows using calculated expressions

In effect, this pattern mimics a nested loop or table expansion operation.

Why This Pattern Exists

DAX does not support procedural loops like for or while.
Instead, iteration happens through table functions.

GENERATE() fills a critical gap by allowing you to:

  • Produce variable numbers of rows per input row
  • Apply row-level calculations while preserving relationships and context

Function Overview

GENERATE

GENERATE (
    table1,
    table2
)
  • table1: The outer table being iterated.
  • table2: A table expression evaluated for each row of table1.

The result is a flattened table containing all rows returned by table2 for every row in table1.

ROW

ROW (
    "ColumnName1", Expression1,
    "ColumnName2", Expression2
)
  • Returns a single-row table
  • Expressions are evaluated in the current row context

When Should You Use the GENERATE / ROW Pattern?

This pattern is ideal when:

✅ You Need to Create Derived Rows

Examples:

  • Generating “Start” and “End” rows per record
  • Creating multiple event types per transaction

✅ You Need Scenario or Category Expansion

Examples:

  • Actual vs Forecast vs Budget rows
  • Multiple pricing or discount scenarios

✅ You Need Row-Level Calculations That Produce Rows

Examples:

  • Expanding date ranges into multiple calculated milestones
  • Generating allocation rows per entity

❌ When Not to Use It

  • Simple aggregations → use SUMX, ADDCOLUMNS
  • Static lookup tables → use calculated tables or Power Query
  • High-volume fact tables without filtering (can be expensive)

Basic Example: Expanding Rows with Labels

Scenario

You have a Sales table:

OrderIDAmount
1100
2200

You want to generate two rows per order:

  • One for Gross
  • One for Net (90% of gross)

DAX Code

Sales Breakdown =
GENERATE (
    Sales,
    ROW (
        "Type", "Gross",
        "Value", Sales[Amount]
    )
    &
    ROW (
        "Type", "Net",
        "Value", Sales[Amount] * 0.9
    )
)

Result

OrderIDTypeValue
1Gross100
1Net90
2Gross200
2Net180

Key Concept: Context Transition

Inside ROW():

  • You are operating in row context
  • Columns from the outer table (Sales) are directly accessible
  • No need for EARLIER() or variables in most cases

This makes the pattern cleaner and easier to reason about.

Intermediate Example: Scenario Modeling

Scenario

You want to model multiple pricing scenarios for each product.

ProductBasePrice
A50
B100

Scenarios:

  • Standard (100%)
  • Discounted (90%)
  • Premium (110%)

DAX Code

Product Pricing Scenarios =
GENERATE (
    Products,
    UNION (
        ROW ( "Scenario", "Standard",   "Price", Products[BasePrice] ),
        ROW ( "Scenario", "Discounted", "Price", Products[BasePrice] * 0.9 ),
        ROW ( "Scenario", "Premium",    "Price", Products[BasePrice] * 1.1 )
    )
)

Result

ProductScenarioPrice
AStandard50
ADiscounted45
APremium55
BStandard100
BDiscounted90
BPremium110

Advanced Example: Date-Based Expansion

Scenario

For each project, generate two milestone rows:

  • Start Date
  • End Date
ProjectStartDateEndDate
X2024-01-012024-03-01

DAX Code

Project Milestones =
GENERATE (
    Projects,
    UNION (
        ROW (
            "Milestone", "Start",
            "Date", Projects[StartDate]
        ),
        ROW (
            "Milestone", "End",
            "Date", Projects[EndDate]
        )
    )
)

This is especially useful for timeline visuals or event-based reporting.

Performance Considerations ⚠️

The GENERATE / ROW pattern can be computationally expensive.

Best Practices

  • Filter the outer table as early as possible
  • Avoid using it on very large fact tables
  • Prefer calculated tables over measures when expanding rows
  • Test with realistic data volumes

Common Mistakes

❌ Using GENERATE When ADDCOLUMNS Is Enough

If you’re only adding columns—not rows—ADDCOLUMNS() is simpler and faster.

❌ Forgetting Table Shape Consistency

All ROW() expressions combined with UNION() must return the same column structure.

❌ Overusing It in Measures

This pattern is usually better suited for calculated tables, not measures.

Mental Model to Remember

Think of the GENERATE / ROW pattern as:

“For each row in this table, generate one or more calculated rows and stack them together.”

If that sentence describes your problem, this pattern is likely the right tool.

Final Thoughts

The GENERATE / ROW pattern is one of those DAX techniques that feels complex at first—but once understood, it unlocks entire classes of modeling and analytical solutions that are otherwise impossible.

Used thoughtfully, it can replace convoluted workarounds, reduce model complexity, and enable powerful scenario-based reporting.

Thanks for reading!

AI, AI Strategy, Analytics, Artificial Intelligence (AI), Data Analysis, Data Careers, Data Education & Training, Data Governance, Data Integration, Data News, Data Science, Data Strategy, Generative AI, Machine Learning (ML), Natural Language Processing (NLP)

AI in Retail and eCommerce: Personalization at Scale Meets Operational Intelligence
“AI in …” series

Retail and eCommerce sit at the intersection of massive data volume, thin margins, and constantly shifting customer expectations. From predicting what customers want to buy next to optimizing global supply chains, AI has become a core capability—not a nice-to-have—for modern retailers.

What makes retail especially interesting is that AI touches both the customer-facing experience and the operational backbone of the business, often at the same time.

How AI Is Being Used in Retail and eCommerce Today

AI adoption in retail spans the full value chain:

Personalized Recommendations & Search

  • Amazon uses machine learning models to power its recommendation engine, driving a significant portion of total sales through “customers also bought” and personalized homepages.
  • Netflix-style personalization, but for shopping: retailers tailor product listings, pricing, and promotions in real time.

Demand Forecasting & Inventory Optimization

  • Walmart applies AI to forecast demand at the store and SKU level, accounting for seasonality, local events, and weather.
  • Target uses AI-driven forecasting to reduce stockouts and overstocks, improving both customer satisfaction and margins.

Dynamic Pricing & Promotions

  • Retailers use AI to adjust prices based on demand, competitor pricing, inventory levels, and customer behavior.
  • Amazon is the most visible example, adjusting prices frequently using algorithmic pricing models.

Customer Service & Virtual Assistants

  • Shopify merchants use AI-powered chatbots for order tracking, returns, and product questions.
  • H&M and Sephora deploy conversational AI for styling advice and customer support.

Fraud Detection & Payments

  • AI models detect fraudulent transactions in real time, especially important for eCommerce and buy-now-pay-later (BNPL) models.

Computer Vision in Physical Retail

  • Amazon Go stores use computer vision, sensors, and deep learning to enable cashierless checkout.
  • Zara (Inditex) uses computer vision to analyze in-store traffic patterns and product engagement.

Tools, Technologies, and Forms of AI in Use

Retailers typically rely on a mix of foundational and specialized AI technologies:

  • Machine Learning & Deep Learning
    Used for forecasting, recommendations, pricing, and fraud detection.
  • Natural Language Processing (NLP)
    Powers chatbots, sentiment analysis of reviews, and voice-based shopping.
  • Computer Vision
    Enables cashierless checkout, shelf monitoring, loss prevention, and in-store analytics.
  • Generative AI & Large Language Models (LLMs)
    Used for product description generation, marketing copy, personalized emails, and internal copilots.
  • Retail AI Platforms
    • Salesforce Einstein for personalization and customer insights
    • Adobe Sensei for content, commerce, and marketing optimization
    • Shopify Magic for product descriptions, FAQs, and merchant assistance
    • AWS, Azure, and Google Cloud AI for scalable ML infrastructure

Benefits Retailers Are Realizing

Retailers that have successfully adopted AI report measurable benefits:

  • Higher Conversion Rates through personalization
  • Improved Inventory Turns and reduced waste
  • Lower Customer Service Costs via automation
  • Faster Time to Market for campaigns and promotions
  • Better Customer Loyalty through more relevant, consistent experiences

In many cases, AI directly links customer experience improvements to revenue growth.

Pitfalls and Challenges

Despite widespread adoption, AI in retail is not without risk:

Bias and Fairness Issues

  • Recommendation and pricing algorithms can unintentionally disadvantage certain customer groups or reinforce biased purchasing patterns.

Data Quality and Fragmentation

  • Poor product data, inconsistent customer profiles, or siloed systems limit AI effectiveness.

Over-Automation

  • Some retailers have over-relied on AI-driven customer service, frustrating customers when human support is hard to reach.

Cost vs. ROI Concerns

  • Advanced AI systems (especially computer vision) can be expensive to deploy and maintain, making ROI unclear for smaller retailers.

Failed or Stalled Pilots

  • AI initiatives sometimes fail because they focus on experimentation rather than operational integration.

Where AI Is Headed in Retail and eCommerce

Several trends are shaping the next phase of AI in retail:

  • Hyper-Personalization
    Experiences tailored not just to the customer, but to the moment—context, intent, and channel.
  • Generative AI at Scale
    Automated creation of product content, marketing campaigns, and even storefront layouts.
  • AI-Driven Merchandising
    Algorithms suggesting what products to carry, where to place them, and how to price them.
  • Blended Physical + Digital Intelligence
    More retailers combining in-store computer vision with online behavioral data.
  • AI as a Copilot for Merchants and Marketers
    Helping teams plan assortments, campaigns, and promotions faster and with more confidence.

How Retailers Can Gain an Advantage

To compete effectively in this fast-moving environment, retailers should:

  1. Focus on Data Foundations First
    Clean product data, unified customer profiles, and reliable inventory systems are essential.
  2. Start with Customer-Critical Use Cases
    Personalization, availability, and service quality usually deliver the fastest ROI.
  3. Balance Automation with Human Oversight
    AI should augment merchandisers, marketers, and store associates—not replace them outright.
  4. Invest in Responsible AI Practices
    Transparency, fairness, and explainability build trust with customers and regulators.
  5. Upskill Retail Teams
    Merchants and marketers who understand AI can use it more creatively and effectively.

Final Thoughts

AI is rapidly becoming the invisible engine behind modern retail and eCommerce. The winners won’t necessarily be the companies with the most advanced algorithms—but those that combine strong data foundations, thoughtful AI governance, and a relentless focus on customer experience.

In retail, AI isn’t just about selling more—it’s about selling smarter, at scale.

Analytics, Artificial Intelligence (AI), Big Data, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Careers, Data Development, Data Education & Training, Data News, Data Science, Microsoft Fabric

Best Data Certifications for 2026

A Quick Guide through some of the top data certifications for 2026

As data platforms continue to converge analytics, engineering, and AI, certifications in 2026 are less about isolated tools and more about end-to-end data value delivery. The certifications below stand out because they align with real-world enterprise needs, cloud adoption, and modern data architectures.

Each certification includes:

  • What it is
  • Why it’s important in 2026
  • How to achieve it
  • Difficulty level

1. DP-600: Microsoft Fabric Analytics Engineer Associate

What it is

DP-600 validates skills in designing, building, and deploying analytics solutions using Microsoft Fabric, including lakehouses, data warehouses, semantic models, and Power BI.

Why it’s important

Microsoft Fabric represents Microsoft’s unified analytics vision, merging data engineering, BI, and governance into a single SaaS platform. DP-600 is quickly becoming one of the most relevant certifications for analytics professionals working in Microsoft ecosystems.

It’s especially valuable because it:

  • Bridges data engineering and analytics
  • Emphasizes business-ready semantic models
  • Aligns directly with enterprise Power BI adoption

How to achieve it

Difficulty level

⭐⭐⭐☆☆ (Intermediate)
Best for analysts or engineers with Power BI or SQL experience.

2. Microsoft Certified: Data Analyst Associate (PL-300)

What it is

A Power BI–focused certification covering data modeling, DAX, visualization, and analytics delivery.

Why it’s important

Power BI remains one of the most widely used BI tools globally. PL-300 proves you can convert data into clear, decision-ready insights.

PL-300 pairs exceptionally well with DP-600 for professionals moving from reporting to full analytics engineering.

How to achieve it

  • Learn Power BI Desktop, DAX, and data modeling
  • Complete hands-on labs
  • Pass the PL-300 exam

Difficulty level

⭐⭐☆☆☆
Beginner to intermediate.

3. Google Data Analytics Professional Certificate

What it is

An entry-level certification covering analytics fundamentals: spreadsheets, SQL, data cleaning, and visualization.

Why it’s important

Ideal for newcomers, this certificate demonstrates foundational data literacy and structured analytical thinking.

How to achieve it

  • Complete the Coursera program
  • Finish hands-on case studies and a capstone

Difficulty level

⭐☆☆☆☆
Beginner-friendly.

4. IBM Data Analyst / IBM Data Science Professional Certificates

What they are

Two progressive certifications:

  • Data Analyst focuses on analytics and visualization
  • Data Science adds Python, ML basics, and modeling

Why they’re important

IBM’s certifications are respected for their hands-on, project-based approach, making them practical for job readiness.

How to achieve them

  • Complete Coursera coursework
  • Submit projects and capstones

Difficulty level

  • Data Analyst: ⭐☆☆☆☆
  • Data Science: ⭐⭐☆☆☆

5. Google Professional Data Engineer

What it is

A certification for building scalable, reliable data pipelines on Google Cloud.

Why it’s important

Frequently ranked among the most valuable data engineering certifications, it focuses on real-world system design rather than memorization.

How to achieve it

  • Learn BigQuery, Dataflow, Pub/Sub, and ML pipelines
  • Gain hands-on GCP experience
  • Pass the professional exam

Difficulty level

⭐⭐⭐⭐☆
Advanced.

6. AWS Certified Data Engineer – Associate

What it is

Validates data ingestion, transformation, orchestration, and storage skills on AWS.

Why it’s important

AWS remains dominant in cloud infrastructure. This certification proves you can build production-grade data pipelines using AWS-native services.

How to achieve it

  • Study Glue, Redshift, Kinesis, Lambda, S3
  • Practice SQL and Python
  • Pass the AWS exam

Difficulty level

⭐⭐⭐☆☆
Intermediate.

7. Microsoft Certified: Fabric Data Engineer Associate (DP-700)

What it is

Focused on data engineering workloads in Microsoft Fabric, including Spark, pipelines, and lakehouse architectures.

Why it’s important

DP-700 complements DP-600 by validating engineering depth within Fabric. Together, they form a powerful Microsoft analytics skill set.

How to achieve it

  • Learn Spark, pipelines, and Fabric lakehouses
  • Pass the DP-700 exam

Difficulty level

⭐⭐⭐☆☆
Intermediate.

8. Databricks Certified Data Engineer Associate

What it is

A certification covering Apache Spark, Delta Lake, and lakehouse architecture using Databricks.

Why it’s important

Databricks is central to modern analytics and AI workloads. This certification signals big data and performance expertise.

How to achieve it

  • Practice Spark SQL and Delta Lake
  • Study Databricks workflows
  • Pass the certification exam

Difficulty level

⭐⭐⭐☆☆
Intermediate.

9. Certified Analytics Professional (CAP)

What it is

A vendor-neutral certification emphasizing analytics lifecycle management, problem framing, and decision-making.

Why it’s important

CAP is ideal for analytics leaders and managers, demonstrating credibility beyond tools and platforms.

How to achieve it

  • Meet experience requirements
  • Pass the CAP exam
  • Maintain continuing education

Difficulty level

⭐⭐⭐⭐☆
Advanced.

10. SnowPro Advanced: Data Engineer

What it is

An advanced Snowflake certification focused on performance optimization, streams, tasks, and advanced architecture.

Why it’s important

Snowflake is deeply embedded in enterprise analytics. This cert signals high-value specialization.

How to achieve it

  • Earn SnowPro Core
  • Gain deep Snowflake experience
  • Pass the advanced exam

Difficulty level

⭐⭐⭐⭐☆
Advanced.

Summary Table

CertificationPrimary FocusDifficulty
DP-600 (Fabric Analytics Engineer)Analytics Engineering⭐⭐⭐☆☆
PL-300BI & Reporting⭐⭐☆☆☆
Google Data AnalyticsEntry Analytics⭐☆☆☆☆
IBM Data Analyst / ScientistAnalytics / DS⭐–⭐⭐
Google Pro Data EngineerCloud DE⭐⭐⭐⭐☆
AWS Data Engineer AssociateCloud DE⭐⭐⭐☆☆
DP-700 (Fabric DE)Data Engineering⭐⭐⭐☆☆
Databricks DE AssociateBig Data⭐⭐⭐☆☆
CAPAnalytics Leadership⭐⭐⭐⭐☆
SnowPro Advanced DESnowflake⭐⭐⭐⭐☆

Final Thoughts

For 2026, the standout trend is clear:

  • Unified platforms (like Microsoft Fabric)
  • Analytics engineering over isolated BI
  • Business-ready data models alongside pipelines

Two of the strongest certification combinations today:

  • DP-600 + PL-300 (analytics) or
  • DP-600 + DP-700 (engineering)

Good luck on your data journey in 2026!

Analytics, Artificial Intelligence (AI), Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Cleaning, Data Development, Data Governance, Data Integration, Data Integration (ETL), Data Modeling, Data Security, Data Strategy, Data Visualization, Data Warehousing, Data Wrangling, Databases, DP-600, Microsoft Certification, Microsoft Fabric, Microsoft OneLake, Performance Tuning, Power BI, Power Query, Python, SQL

Exam Prep Hub for DP-600: Implementing Analytics Solutions Using Microsoft Fabric

This is your one-stop hub with information for preparing for the DP-600: Implementing Analytics Solutions Using Microsoft Fabric certification exam. Upon successful completion of the exam, you earn the Fabric Analytics Engineer Associate certification.

This hub provides information directly here, links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the exam and using as many of the resources available as possible. We hope you find it convenient and helpful.

Why do the DP-600: Implementing Analytics Solutions Using Microsoft Fabric exam to gain the Fabric Analytics Engineer Associate certification?

Most likely, you already know why you want to earn this certification, but in case you are seeking information on its benefits, here are a few:
(1) there is a possibility for career advancement because Microsoft Fabric is a leading data platform used by companies of all sizes, all over the world, and is likely to become even more popular
(2) greater job opportunities due to the edge provided by the certification
(3) higher earnings potential,
(4) you will expand your knowledge about the Fabric platform by going beyond what you would normally do on the job and
(5) it will provide immediate credibility about your knowledge, and
(6) it may, and it should, provide you with greater confidence about your knowledge and skills.

Important DP-600 resources:

DP-600: Skills measured as of October 31, 2025:

Here you can learn in a structured manner by going through the topics of the exam one-by-one to ensure full coverage; click on each hyperlinked topic below to go to more information about it:

Skills at a glance

  • Maintain a data analytics solution (25%-30%)
  • Prepare data (45%-50%)
  • Implement and manage semantic models (25%-30%)

Maintain a data analytics solution (25%-30%)

Implement security and governance

Maintain the analytics development lifecycle

Prepare data (45%-50%)

Get Data

Transform Data

Query and analyze data

Implement and manage semantic models (25%-30%)

Design and build semantic models

Optimize enterprise-scale semantic models

Practice Exams:

We have provided 2 practice exams with answers to help you prepare.

DP-600 Practice Exam 1 (60 questions with answer key)

DP-600 Practice Exam 2 (60 questions with answer key)

Good luck to you passing the DP-600: Implementing Analytics Solutions Using Microsoft Fabric certification exam and earning the Fabric Analytics Engineer Associate certification!

Analytics, BI Administration, Big Data, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Governance, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Visualization, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting, SQL

Implement Performance Improvements in Queries and Report Visuals (DP-600 Exam Prep)

 
This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Implement and manage semantic models (25-30%) 
    --> Optimize enterprise-scale semantic models 
        --> Implement performance improvements in queries and report visuals

Performance optimization is a critical skill for the Fabric Analytics Engineer. In enterprise-scale semantic models, poor query design, inefficient DAX, or overly complex visuals can significantly degrade report responsiveness and user experience. This exam section focuses on identifying performance bottlenecks and applying best practices to improve query execution, model efficiency, and report rendering.

1. Understand Where Performance Issues Occur

Performance problems typically fall into three layers:

a. Data & Storage Layer

  • Storage mode (Import, DirectQuery, Direct Lake, Composite)
  • Data source latency
  • Table size and cardinality
  • Partitioning and refresh strategies

b. Semantic Model & Query Layer

  • DAX calculation complexity
  • Relationships and filter propagation
  • Aggregation design
  • Use of calculation groups and measures

c. Report & Visual Layer

  • Number and type of visuals
  • Cross-filtering behavior
  • Visual-level queries
  • Use of slicers and filters

DP-600 questions often test your ability to identify the correct layer where optimization is needed.

2. Optimize Queries and Semantic Model Performance

a. Choose the Appropriate Storage Mode

  • Use Import for small-to-medium datasets requiring fast interactivity
  • Use Direct Lake for large OneLake Delta tables with high concurrency
  • Use Composite models to balance performance and real-time access
  • Avoid unnecessary DirectQuery when Import or Direct Lake is feasible

b. Reduce Data Volume

  • Remove unused columns and tables
  • Reduce column cardinality (e.g., avoid high-cardinality text columns)
  • Prefer surrogate keys over natural keys
  • Disable Auto Date/Time when not needed

c. Optimize Relationships

  • Use single-direction relationships by default
  • Avoid unnecessary bidirectional filters
  • Ensure relationships follow a star schema
  • Avoid many-to-many relationships unless required

d. Use Aggregations

  • Create aggregation tables to pre-summarize large fact tables
  • Enable query hits against aggregation tables before scanning detailed data
  • Especially valuable in composite models

3. Improve DAX Query Performance

a. Write Efficient DAX

  • Prefer measures over calculated columns
  • Use variables (VAR) to avoid repeated calculations
  • Minimize row context where possible
  • Avoid excessive iterators (SUMX, FILTER) over large tables

b. Use Filter Context Efficiently

  • Prefer CALCULATE with simple filters
  • Avoid complex nested FILTER expressions
  • Use KEEPFILTERS and REMOVEFILTERS intentionally

c. Avoid Expensive Patterns

  • Avoid EARLIER in favor of variables
  • Avoid dynamic table generation inside visuals
  • Minimize use of ALL when ALLSELECTED or scoped filters suffice

4. Optimize Report Visual Performance

a. Reduce Visual Complexity

  • Limit the number of visuals per page
  • Avoid visuals that generate multiple queries (e.g., complex custom visuals)
  • Use summary visuals instead of detailed tables where possible

b. Control Interactions

  • Disable unnecessary visual interactions
  • Avoid excessive cross-highlighting
  • Use report-level filters instead of visual-level filters when possible

c. Optimize Slicers

  • Avoid slicers on high-cardinality columns
  • Use dropdown slicers instead of list slicers
  • Limit the number of slicers on a page

d. Prefer Measures Over Visual Calculations

  • Avoid implicit measures created by dragging numeric columns
  • Define explicit measures in the semantic model
  • Reuse measures across visuals to improve cache efficiency

5. Use Performance Analysis Tools

a. Performance Analyzer

  • Identify slow visuals
  • Measure DAX query duration
  • Distinguish between query time and visual rendering time

b. Query Diagnostics (Power BI Desktop)

  • Analyze backend query behavior
  • Identify expensive DirectQuery or Direct Lake operations

c. DAX Studio (Advanced)

  • Analyze query plans
  • Measure storage engine vs formula engine time
  • Identify inefficient DAX patterns

(You won’t be tested on tool UI details, but knowing when and why to use them is exam-relevant.)

6. Common DP-600 Exam Scenarios

You may be asked to:

  • Identify why a report is slow and choose the best optimization
  • Identify the bottleneck layer (model, query, or visual)
  • Select the most appropriate storage mode for performance
  • Choose the least disruptive, most effective optimization
  • Improve a slow DAX measure
  • Reduce visual rendering time without changing the data source
  • Optimize performance for enterprise-scale models
  • Apply enterprise-scale best practices, not just quick fixes

Key Exam Takeaways

  • Always optimize the model first, visuals second
  • Star schema + clean relationships = better performance
  • Efficient DAX matters more than clever DAX
  • Fewer visuals and interactions = faster reports
  • Aggregations and Direct Lake are key enterprise-scale tools

Practice Questions:

Go to the Practice Exam Questions for this topic.

Analytics, Business Intelligence, Business Intelligence (BI) Development, Data Analysis, Data Development, Data Integration, Data Modeling, Data Quality Assurance, Data Security, Data Strategy, DP-600, Microsoft Certification, Microsoft Fabric, Performance Tuning, Power BI, Power Query, Reporting, SQL

Design and Build Composite Models (DP-600 Exam Prep)

 
This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Implement and manage semantic models (25-30%) 
    --> Design and build semantic models 
        --> Design and Build Composite Models

What Is a Composite Model?

A composite model in Power BI and Microsoft Fabric combines data from multiple data sources and multiple storage modes in a single semantic model. Rather than importing all data into the model’s in-memory cache, composite models let you mix different query/storage patterns such as:

  • Import
  • DirectQuery
  • Direct Lake
  • Live connections

Composite models enable flexible design and optimized performance across diverse scenarios.

Why Composite Models Matter

Semantic models often need to support:

  • Large datasets that cannot be imported fully
  • Real-time or near-real-time requirements
  • Federation across disparate sources
  • Mix of highly dynamic and relatively static data

Composite models let you combine the benefits of in-memory performance with direct source access.

Core Concepts

Storage Modes in Composite Models

Storage ModeDescriptionTypical Use
ImportData is cached in the semantic model memoryFast performance for static or moderately sized data
DirectQueryQueries are pushed to the source at runtimeReal-time or large relational sources
Direct LakeQueries Delta tables in OneLakeLarge OneLake data with faster interactive access
Live ConnectionDelegates all query processing to an external modelShared enterprise semantic models

A composite model may include tables using different modes — for example, imported dimension tables and DirectQuery/Direct Lake fact tables.

Key Features of Composite Models

1. Table-Level Storage Modes

Every table in a composite model may use a different storage mode:

  • Dimensions may be imported
  • Fact tables may use DirectQuery or Direct Lake
  • Bridge or helper tables may be imported

This flexibility enables performance and freshness trade-offs.

2. Relationships Across Storage Modes

Relationships can span tables even if they use different storage modes, enabling:

  • Filtering between imported and DirectQuery tables
  • Cross-mode joins (handled intelligently by the engine)

Underlying engines push queries to the appropriate source (SQL, OneLake, Semantic layer), depending on where the data resides.

3. Aggregations and Hierarchies

You can define:

  • Aggregated tables (pre-summarized import tables)
  • Detail tables (DirectQuery or Direct Lake)

Power BI automatically uses aggregations when a visual’s query can be satisfied with summary data, enhancing performance.

4. Calculation Groups and Measures

Composite models work with complex semantic logic:

  • Calculation groups (standardized transformations)
  • DAX measures that span imported and DirectQuery tables

These models require careful modeling to ensure that context transitions behave predictably.

When to Use Composite Models

Composite models are ideal when:

A. Data Is Too Large to Import

  • Large fact tables (> hundreds of millions of rows)
  • Delta/OneLake data too big for full in-memory import
  • Use Direct Lake for these, while importing dimensions

B. Real-Time Data Is Required

  • Operational reporting
  • Systems with high update frequency
  • Use DirectQuery to relational sources

C. Multiple Data Sources Must Be Combined

  • Relational databases
  • OneLake & Delta
  • Cloud services (e.g., Synapse, SQL DB, Spark)
  • On-prem gateways

Composite models let you combine these seamlessly.

D. Different Performance vs Freshness Needs

  • Import for static master data
  • DirectQuery or Direct Lake for dynamic fact data

Composite vs Pure Models

AspectImport OnlyComposite
PerformanceVery fastDepends on source/query pattern
FreshnessScheduled refreshReal-time/near-real-time possible
Source diversityLimitedMultiple heterogeneous sources
Model complexitySimplerHigher

Query Execution and Optimization

Query Folding

  • DirectQuery and Power Query transformations rely on query folding to push logic back to the source
  • Query folding is essential for performance in composite models

Storage Mode Selection

Good modeling practices for composite models include:

  • Import small dimension tables
  • Direct Lake for large storage in OneLake
  • DirectQuery for real-time relational sources
  • Use aggregations to optimize performance

Modeling Considerations

1. Relationship Direction

  • Prefer single-direction relationships
  • Use bidirectional filtering only when required (careful with ambiguity)

2. Data Type Consistency

  • Ensure fields used in joins have matching data types
  • In composite models, mismatches can cause query fallbacks

3. Cardinality

  • High cardinality DirectQuery columns can slow queries
  • Use star schema patterns

4. Security

  • Row-level security crosses modes but must be carefully tested
  • Security logic must consider where filters are applied

Common Exam Scenarios

Exam questions may ask you to:

  • Choose between Import, DirectQuery, Direct Lake and composite
  • Assess performance vs freshness requirements
  • Determine query folding feasibility
  • Identify correct relationship patterns across modes

Example prompt:

“Your model combines a large OneLake dataset and a small dimension table. Users need current data daily but also fast filtering. Which storage and modeling approach is best?”

Correct exam choices often point to composite models using Direct Lake + imported dimensions.

Best Practices

  • Define a clear star schema even in composite models
  • Import dimension tables where reasonable
  • Use aggregations to improve performance for heavy visuals
  • Limit direct many-to-many relationships
  • Use calculation groups to apply analytics consistently
  • Test query performance across storage modes

Exam-Ready Summary/Tips

Composite models enable flexible and scalable semantic models by mixing storage modes:

  • Import – best performance for static or moderate data
  • DirectQuery – real-time access to source systems
  • Direct Lake – scalable querying of OneLake Delta data
  • Live Connection – federated or shared datasets

Design composite models to balance performance, freshness, and data volume, using strong schema design and query optimization.

For DP-600, always evaluate:

  • Data volume
  • Freshness requirements
  • Performance expectations
  • Source location (OneLake vs relational)

Composite models are frequently the correct answer when these requirements conflict.

Practice Questions:

Here are 10 questions to test and help solidify your learning and knowledge. As you review these and other questions in your preparation, make sure to …

  • Identifying and understand why an option is correct (or incorrect) — not just which one
  • Look for and understand the usage scenario of keywords in exam questions to guide you
  • Expect scenario-based questions rather than direct definitions

1. What is the primary purpose of using a composite model in Microsoft Fabric?

A. To enable row-level security across workspaces
B. To combine multiple storage modes and data sources in one semantic model
C. To replace DirectQuery with Import mode
D. To enforce star schema design automatically

✅ Correct Answer: B

Explanation:
Composite models allow you to mix Import, DirectQuery, Direct Lake, and Live connections within a single semantic model, enabling flexible performance and data-freshness tradeoffs.

2. You are designing a semantic model with a very large fact table stored in OneLake and small dimension tables. Which storage mode combination is most appropriate?

A. Import all tables
B. DirectQuery for all tables
C. Direct Lake for the fact table and Import for dimension tables
D. Live connection for the fact table and Import for dimensions

✅ Correct Answer: C

Explanation:
Direct Lake is optimized for querying large Delta tables in OneLake, while importing small dimension tables improves performance for filtering and joins.

3. Which storage mode allows querying OneLake Delta tables without importing data into memory?

A. Import
B. DirectQuery
C. Direct Lake
D. Live Connection

✅ Correct Answer: C

Explanation:
Direct Lake queries Delta tables directly in OneLake, combining scalability with better interactive performance than traditional DirectQuery.

4. What happens when a DAX query in a composite model references both imported and DirectQuery tables?

A. The query fails
B. The data must be fully imported
C. The engine generates a hybrid query plan
D. All tables are treated as DirectQuery

✅ Correct Answer: C

Explanation:
Power BI’s engine generates a hybrid query plan, pushing operations to the source where possible and combining results with in-memory data.

5. Which scenario most strongly justifies using a composite model instead of Import mode only?

A. All data fits in memory and refreshes nightly
B. The dataset is static and small
C. Users require near-real-time data from a large relational source
D. The model contains only calculated tables

✅ Correct Answer: C

Explanation:
Composite models are ideal when real-time or near-real-time access is needed, especially for large datasets that are impractical to import.

6. In a composite model, which table type is typically best suited for Import mode?

A. High-volume transactional fact tables
B. Streaming event tables
C. Dimension tables with low cardinality
D. Tables requiring second-by-second freshness

✅ Correct Answer: C

Explanation:
Importing dimension tables improves query performance and reduces load on source systems due to their relatively small size and low volatility.

7. How do aggregation tables improve performance in composite models?

A. By replacing DirectQuery with Import
B. By pre-summarizing data to satisfy queries without scanning detail tables
C. By eliminating the need for relationships
D. By enabling bidirectional filtering automatically

✅ Correct Answer: B

Explanation:
Aggregations allow Power BI to answer queries using pre-summarized Import tables, avoiding expensive queries against large DirectQuery or Direct Lake fact tables.

8. Which modeling pattern is strongly recommended when designing composite models?

A. Snowflake schema
B. Flat tables
C. Star schema
D. Many-to-many relationships

✅ Correct Answer: C

Explanation:
A star schema simplifies relationships, improves performance, and reduces ambiguity—especially important in composite and cross-storage-mode models.

9. What is a potential risk of excessive bidirectional relationships in composite models?

A. Reduced data freshness
B. Increased memory consumption
C. Ambiguous filter paths and unpredictable query behavior
D. Loss of row-level security

✅ Correct Answer: C

Explanation:
Bidirectional relationships can introduce ambiguity, cause unexpected filtering, and negatively affect query performance—risks that are amplified in composite models.

10. Which feature allows a composite model to reuse an enterprise semantic model while extending it with additional data?

A. Direct Lake
B. Import mode
C. Live connection with local tables
D. Calculation groups

✅ Correct Answer: C

Explanation:
A live connection with local tables enables extending a shared enterprise semantic model by adding new tables and measures, forming a composite model.