Category: DP-900

Data Engineering, DP-900, Microsoft Certification May 10, 2026

Practice Questions: Describe responsibilities for data engineers (DP-900 Exam Prep)

Practice Questions

Question 1

Which task is a primary responsibility of a data engineer?

A. Creating dashboards for business users
B. Managing database user permissions
C. Building and maintaining data pipelines
D. Training machine learning models

✅ Answer: C

Explanation:
Data engineers are responsible for designing and maintaining data pipelines that move and transform data.

Question 2

A company needs to collect data from multiple systems and prepare it for reporting.

Which role is primarily responsible for this task?

A. Data Analyst
B. Database Administrator
C. Data Engineer
D. Business User

✅ Answer: C

Explanation:
Data engineers handle data ingestion, integration, and preparation for downstream analytics.

Question 3

Which process involves extracting data from sources, transforming it, and loading it into a destination system?

A. OLTP
B. ETL
C. OLAP
D. ACID

✅ Answer: B

Explanation:
ETL (Extract, Transform, Load) is a core responsibility of data engineers.

Question 4

Which Azure service is commonly used by data engineers to orchestrate data pipelines?

A. Azure SQL Database
B. Azure Data Factory
C. Azure Blob Storage
D. Azure Virtual Machines

✅ Answer: B

Explanation:
Azure Data Factory is used to build, schedule, and manage data pipelines.

Question 5

Which responsibility ensures that data used for analytics is accurate and reliable?

A. Query optimization
B. Data visualization
C. Data quality management
D. User authentication

✅ Answer: C

Explanation:
Data engineers ensure data quality through validation and cleaning processes.

Question 6

A data engineer is working with large-scale data processing using Apache Spark.

Which Azure service are they MOST likely using?

A. Azure SQL Database
B. Azure Cosmos DB
C. Azure Databricks
D. Azure Table Storage

✅ Answer: C

Explanation:
Azure Databricks is a Spark-based platform used for large-scale data processing.

Question 7

Which storage solution is commonly used by data engineers for storing large volumes of raw and processed data?

A. Azure Data Lake Storage
B. Azure Queue Storage
C. Azure SQL Database
D. Azure Cache for Redis

✅ Answer: A

Explanation:
Azure Data Lake Storage is optimized for big data storage and analytics workloads.

Question 8

Which task is LEAST likely to be performed by a data engineer?

A. Transforming raw data into structured formats
B. Monitoring data pipelines
C. Creating Power BI dashboards
D. Integrating multiple data sources

✅ Answer: C

Explanation:
Creating dashboards is typically the responsibility of a data analyst, not a data engineer.

Question 9

Which type of data processing involves handling real-time data streams?

A. Batch processing
B. Streaming processing
C. Relational processing
D. Transactional processing

✅ Answer: B

Explanation:
Data engineers often work with streaming pipelines for real-time data ingestion.

Question 10

A data engineer selects Parquet as a storage format for a dataset.

What is the primary reason for this choice?

A. It is human readable
B. It supports transactional updates
C. It is optimized for analytical performance
D. It enforces a strict schema

✅ Answer: C

Explanation:
Parquet is a columnar format that improves performance for analytical workloads.

✅ Quick Exam Takeaways

For DP-900, remember data engineers:

✔ Build and manage data pipelines
✔ Handle ETL/ELT processes
✔ Work with batch and streaming data
✔ Ensure data quality and reliability
✔ Manage data storage solutions (Data Lake, Blob)
✔ Use Azure services like:

Azure Data Factory
Azure Databricks
Azure Data Lake Storage
Azure Synapse Analytics

✔ Enable analytics and BI by preparing data

Go to the DP-900 Exam Prep Hub main page.

Analytics, Cloud computing, DP-900, Microsoft Certification May 10, 2026May 10, 2026

Describe Microsoft Cloud Services for large-scale analytics (Azure Databricks & Microsoft Fabric) (DP-900 Exam Prep)

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe common elements of large-scale analytics
      --> Describe Microsoft Cloud Services for large-scale analytics (Azure Databricks & Microsoft Fabric)

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Modern analytics workloads often require processing massive volumes of data quickly and efficiently. Microsoft provides powerful cloud services to meet these needs, including Azure Databricks and Microsoft Fabric.

For the DP-900 exam, you should understand what these services are, their key features, and when to use each.

Why Large-Scale Analytics Services Matter

Large-scale analytics involves:

Processing big data (TBs to PBs)
Supporting batch and real-time workloads
Enabling advanced analytics and machine learning

✔ Traditional tools often cannot scale to meet these demands.

Azure Databricks

What Is Azure Databricks?

Azure Databricks is a cloud-based analytics platform built on Apache Spark.

It is designed for:

Big data processing
Data engineering
Machine learning
Collaborative analytics

Key Features

1. Apache Spark-Based Processing

Distributed computing engine
Processes large datasets in parallel

✔ Ideal for big data workloads

2. Collaborative Workspace

Notebooks (Python, SQL, Scala, R)
Multiple users can collaborate

3. Integration with Azure

Works with Azure Data Lake Storage
Integrates with Azure Synapse Analytics

4. Machine Learning Support

Built-in ML capabilities
Supports advanced analytics workflows

Common Use Cases

Big data processing (ETL/ELT pipelines)
Data science and machine learning
Real-time analytics
Data transformation at scale

✔ Best for: Data engineers and data scientists working with large datasets

Microsoft Fabric

What Is Microsoft Fabric?

Microsoft Fabric is an end-to-end, unified analytics platform that brings together multiple data services into a single environment.

It integrates:

Data engineering
Data warehousing
Data science
Real-time analytics
Business intelligence

Key Features

1. Unified Platform

Combines multiple services into one
Reduces complexity of managing separate tools

2. OneLake (Unified Storage Layer)

Centralized data lake for all workloads
Eliminates data silos

3. Integrated Analytics Experiences

Data Factory (ingestion)
Data Warehouse
Real-Time Analytics
Power BI integration

4. SaaS-Based Model

Fully managed platform
Minimal infrastructure management

Common Use Cases

End-to-end analytics solutions
Unified data platform for organizations
Business intelligence and reporting
Data integration and transformation

✔ Best for: Organizations wanting a single, unified analytics solution

Azure Databricks vs Microsoft Fabric

Feature	Azure Databricks	Microsoft Fabric
Focus	Big data processing & ML	End-to-end analytics platform
Engine	Apache Spark	Multiple integrated engines
Users	Data engineers, data scientists	Broad (engineers, analysts, business users)
Complexity	More flexible, more technical	Simpler, unified experience
Use Case	Advanced analytics & ML	Unified analytics and BI

How They Fit in an Analytics Architecture

Typical roles:

Azure Databricks
- Data processing
- Advanced transformations
- Machine learning
Microsoft Fabric
- End-to-end pipeline
- Storage (OneLake)
- Reporting (Power BI integration)

✔ They can complement each other in modern architectures.

Key Considerations When Choosing

Choose Azure Databricks when:

You need advanced data engineering or machine learning
You require Spark-based processing
You want full control and flexibility

Choose Microsoft Fabric when:

You want a unified analytics platform
You prefer simplified, integrated workflows
You need end-to-end analytics in one place

Why This Matters for DP-900

On the exam, you may be asked to:

Identify the purpose of Azure Databricks
Recognize Microsoft Fabric as a unified analytics platform
Choose the right service for a scenario
Understand how these services support large-scale analytics

Summary — Exam-Relevant Takeaways

✔ Azure Databricks

Apache Spark-based
Big data processing
Machine learning
Flexible and powerful

✔ Microsoft Fabric

Unified analytics platform
End-to-end solution
Includes data engineering, warehousing, and BI

✔ Key difference:

Databricks → advanced processing & ML
Fabric → all-in-one analytics platform

✔ Exam tip:
👉 Spark + big data processing → Azure Databricks
👉 Unified analytics platform → Microsoft Fabric

Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.

Analytics, azure, Data Engineering, DP-900, Microsoft Certification May 10, 2026May 10, 2026

Describe the difference between Batch and Streaming data (DP-900 Exam Prep)

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe considerations for real-time data analytics
      --> Describe the difference between Batch and Streaming data

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Understanding the difference between batch data and streaming data is fundamental for designing modern analytics solutions. These two approaches define how data is ingested, processed, and analyzed.

What Is Batch Data?

Batch data refers to data that is:

Collected over a period of time
Processed in large chunks (batches)
Handled at scheduled intervals

Key Characteristics of Batch Data

High latency (minutes, hours, or days)
Processes large volumes at once
Typically scheduled (e.g., nightly jobs)
Efficient and cost-effective

Common Use Cases

Daily sales reports
Monthly financial summaries
Historical data analysis
Data warehousing workloads

Azure Services for Batch Processing

Azure Data Factory → batch ingestion and orchestration
Azure Synapse Analytics → batch processing and analytics

What Is Streaming Data?

Streaming data refers to data that is:

Generated continuously
Processed in real time (or near real time)
Handled as individual events or small micro-batches

Key Characteristics of Streaming Data

Low latency (seconds or milliseconds)
Continuous data flow
Enables real-time insights
Often requires more complex processing

Common Use Cases

IoT sensor monitoring
Fraud detection
Live dashboards
Website activity tracking

Azure Services for Streaming

Azure Event Hubs → event ingestion
Azure Stream Analytics → real-time processing

Batch vs Streaming — Key Differences

Feature	Batch Processing	Streaming Processing
Data Flow	Periodic	Continuous
Latency	High	Low
Data Size	Large chunks	Small events
Complexity	Simpler	More complex
Cost	Lower	Higher
Use Case	Historical analysis	Real-time insights

When to Use Batch Processing

Choose batch when:

Real-time data is not required
You are working with large historical datasets
Cost efficiency is important
Processing can occur on a schedule

When to Use Streaming Processing

Choose streaming when:

You need real-time or near real-time insights
Data is generated continuously
Immediate action is required

Hybrid Approaches (Lambda / Modern Architectures)

Many modern systems use both:

Batch layer → historical analysis
Streaming layer → real-time insights

✔ Example:

Real-time dashboard + nightly aggregated reports

Why This Matters for DP-900

On the exam, you may be asked to:

Distinguish between batch and streaming scenarios
Choose the appropriate processing method
Identify Azure services for each approach
Understand trade-offs (latency, cost, complexity)

Summary — Exam-Relevant Takeaways

✔ Batch processing

Processes data in chunks
Higher latency
Lower cost
Best for historical analysis

✔ Streaming processing

Processes data continuously
Low latency
Enables real-time insights
More complex

✔ Azure services:

Batch → Azure Data Factory, Azure Synapse Analytics
Streaming → Azure Event Hubs, Azure Stream Analytics

✔ Exam tip:
👉 Real-time requirement → Streaming
👉 Scheduled / historical → Batch

Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.

Data Visualization, DP-900, Microsoft Certification, Power BI May 10, 2026

Identify appropriate visualizations for data (DP-900 Exam Prep)

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe data visualization in Microsoft Power BI
      --> Identify appropriate visualizations for data

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Data visualization is the process of representing data graphically so users can quickly understand patterns, trends, relationships, and insights. In Microsoft Power BI, choosing the correct visualization is important for effective reporting and decision-making.

For the DP-900 exam, you should understand:

Common visualization types
When each visualization should be used
The strengths and limitations of different visuals

Why Visualization Selection Matters

The correct visualization helps users:

Understand data quickly
Identify trends and anomalies
Compare values
Monitor performance
Make informed decisions

Using the wrong visualization can make data confusing or misleading.

Common Visualization Types in Power BI

1. Bar Charts and Column Charts

Purpose

Used to compare values across categories.

Best Used For

Comparing sales by region
Comparing revenue by product
Ranking categories

Difference

Bar chart → horizontal bars
Column chart → vertical bars

Advantages

✔ Easy to read
✔ Good for comparisons
✔ Works well with categorical data

Example

Sales by product category

2. Line Charts

Purpose

Used to show trends over time.

Best Used For

Monthly sales trends
Website traffic over time
Stock price movement

Advantages

✔ Excellent for time-series data
✔ Clearly shows increases/decreases

Example

Revenue by month

3. Pie Charts and Donut Charts

Purpose

Show proportions or percentages of a whole.

Best Used For

Market share
Percentage of sales by region

Limitations

❌ Difficult with many categories
❌ Hard to compare similar values

Best Practice

Use only with a small number of categories

4. Tables and Matrices

Tables

Purpose

Display detailed data in rows and columns.

Best Used For

Exact values
Detailed records

Matrices

Purpose

Similar to pivot tables with grouped summaries.

Best Used For

Aggregated business reporting
Cross-tab analysis

Advantages

✔ Good for detailed analysis
✔ Supports drill-down

5. Maps

Purpose

Visualize geographic data.

Best Used For

Sales by country
Store locations
Regional performance

Requirements

Data should contain:

Country
City
Coordinates

6. KPI Visuals

Purpose

Display performance against goals.

Best Used For

Revenue targets
Operational metrics
Performance monitoring

Advantages

✔ Easy to monitor status
✔ Quickly highlights success/failure

7. Gauge Charts

Purpose

Show progress toward a target value.

Best Used For

Budget usage
Performance thresholds

Example

Current sales vs sales target

8. Scatter Charts

Purpose

Show relationships between two numeric variables.

Best Used For

Correlation analysis
Identifying outliers

Example

Advertising spend vs revenue

9. Cards

Purpose

Display a single key metric.

Best Used For

Total revenue
Customer count
Profit margin

Advantages

✔ Simple and clear
✔ Common in dashboards

10. Slicers

Purpose

Provide interactive filtering.

Best Used For

Filtering by date
Selecting regions or categories

Advantages

✔ Enhances report interactivity

Choosing the Right Visualization

Goal	Recommended Visualization
Compare categories	Bar/Column Chart
Show trends over time	Line Chart
Show proportions	Pie/Donut Chart
Display exact values	Table
Summarize grouped data	Matrix
Show geographic data	Map
Track KPIs	KPI/Gauge
Show correlations	Scatter Chart
Show a single metric	Card

Visualization Best Practices

Keep Visuals Simple

Avoid clutter and unnecessary complexity.

Use Appropriate Colors

Colors should improve readability, not distract.

Limit Pie Chart Categories

Too many slices reduce readability.

Use Consistent Formatting

Helps users interpret reports more easily.

Focus on Business Questions

Choose visuals that answer specific questions.

Interactive Features in Power BI

Power BI visuals support:

Filtering
Drill-down
Cross-highlighting
Tooltips

These features make reports interactive and user-friendly.

Why This Matters for DP-900

On the exam, you may be asked to:

Identify the best visualization for a scenario
Match visualization types to business requirements
Understand the strengths and weaknesses of visuals

Summary — Exam-Relevant Takeaways

✔ Common visuals:

Bar/Column → comparisons
Line → trends over time
Pie/Donut → proportions
Map → geographic data
Scatter → relationships
Card → single metric

✔ Tables show detailed data

✔ KPIs and gauges track performance

✔ Slicers provide interactivity

✔ Exam tips:
👉 Line chart = trends over time
👉 Bar chart = category comparison
👉 Pie chart = parts of a whole
👉 Scatter chart = relationships/correlation
👉 Card = single value

Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.

Data Visualization, DP-900, Microsoft Certification, Power BI May 10, 2026

Additional information: Visualization comparison table (DP-900 Exam Prep)

The table below serves as a tool that can be used to quickly compare and contrast the various visualization types available in Power BI.

Visualization Comparison Table

Chart Type	Purpose	Best Used For	Advantages	Differences	Best Practice	Example
Bar Chart	Compare values across categories	Comparing sales by region, revenue by product, ranking categories	Easy to read; excellent for comparisons	Uses horizontal bars	Use for categorical comparisons with many category labels	Sales by product category
Column Chart	Compare values across categories	Comparing monthly revenue, product performance, department comparisons	Clear visual comparisons; familiar layout	Uses vertical bars	Ideal when category names are short	Revenue by department
Line Chart	Show trends over time	Monthly sales trends, stock prices, website traffic	Excellent for time-series analysis; clearly shows increases/decreases	Focuses on continuous progression over time	Use with dates or sequential data	Revenue by month
Pie Chart	Show proportions of a whole	Market share, percentage contribution by region	Easy to understand with small datasets	Circular chart divided into slices	Limit to a small number of categories	Percentage of sales by region
Donut Chart	Show proportions of a whole	Similar use cases as pie charts	Modern appearance; center area can display totals	Similar to pie chart but with a hollow center	Avoid too many slices	Product category contribution percentages
Table	Display detailed data	Transaction records, exact values	Shows precise values; supports detailed analysis	Displays raw row-and-column data	Use when exact figures are important	Customer order list
Matrix	Summarize grouped data	Cross-tab analysis, business summaries	Supports grouping and drill-down	Similar to a pivot table	Use for summarized reporting	Sales by region and product
Map	Visualize geographic data	Sales by country, store locations, regional analysis	Excellent for location-based insights	Uses geographic plotting	Ensure geographic fields are accurate	Revenue by state
KPI Visual	Display performance against goals	Revenue targets, operational metrics	Quickly shows status and performance	Focuses on KPI indicators and trends	Use for executive dashboards	Monthly sales target status
Gauge Chart	Show progress toward a target	Budget usage, performance thresholds	Easy to interpret progress toward goals	Circular meter-style visualization	Use for single-metric target tracking	Current sales vs target
Scatter Chart	Show relationships between variables	Correlation analysis, identifying outliers	Helps identify patterns and relationships	Plots points using two numeric axes	Use with numeric datasets	Advertising spend vs revenue
Card	Display a single key metric	Total revenue, customer count, profit margin	Very simple and clear	Displays one summarized value	Use for important KPIs	Total Sales
Slicer	Provide interactive filtering	Filtering by date, region, category	Enhances report interactivity	Functions as a filter control rather than a chart	Keep slicers simple and intuitive	Region selection filter

Go to the DP-900 Exam Prep Hub main page.

Data Visualization, DP-900, Microsoft Certification, Power BI May 10, 2026

Practice Questions: Identify appropriate visualizations for data (DP-900 Exam Prep)

Practice Questions

Question 1

Which visualization is BEST for showing sales trends over the past 12 months?

A. Pie chart
B. Scatter chart
C. Line chart
D. Gauge chart

✅ Answer: C

Explanation:
Line charts are ideal for displaying trends over time.

Question 2

Which visualization is MOST appropriate for comparing revenue across different product categories?

A. Map
B. Bar chart
C. Card
D. Gauge chart

✅ Answer: B

Explanation:
Bar charts are commonly used for comparing values across categories.

Question 3

Which visualization is BEST suited for displaying a single metric such as total profit?

A. Matrix
B. Pie chart
C. Card
D. Scatter chart

✅ Answer: C

Explanation:
Cards are used to display a single important value or KPI.

Question 4

Which visualization would be MOST useful for displaying sales performance by country?

A. Map
B. Gauge chart
C. Pie chart
D. Card

✅ Answer: A

Explanation:
Maps are designed for visualizing geographic or location-based data.

Question 5

Which visualization is BEST for showing the relationship between advertising spend and sales revenue?

A. Line chart
B. Scatter chart
C. Pie chart
D. Matrix

✅ Answer: B

Explanation:
Scatter charts help identify relationships and correlations between numeric variables.

Question 6

A report needs to show how each region contributes to total company sales. Which visualization is MOST appropriate?

A. Gauge chart
B. Pie chart
C. Table
D. Scatter chart

✅ Answer: B

Explanation:
Pie charts are commonly used to show proportions or percentages of a whole.

Question 7

Which Power BI visualization is MOST appropriate for displaying detailed transactional records?

A. Table
B. Card
C. Gauge chart
D. Pie chart

✅ Answer: A

Explanation:
Tables display detailed row-level data effectively.

Question 8

Which visualization is BEST for monitoring progress toward a sales target?

A. Scatter chart
B. Matrix
C. Gauge chart
D. Pie chart

✅ Answer: C

Explanation:
Gauge charts show progress toward a goal or threshold.

Question 9

Which Power BI feature allows users to interactively filter report visuals?

A. Relationships
B. Measures
C. Slicers
D. Cards

✅ Answer: C

Explanation:
Slicers provide interactive filtering capabilities in reports.

Question 10

Which visualization is MOST appropriate for summarized cross-tab reporting with grouped data?

A. Matrix
B. Card
C. Pie chart
D. Gauge chart

✅ Answer: A

Explanation:
Matrices are useful for grouped summaries and pivot-style analysis.

✅ Quick Exam Takeaways

✔ Visualization selection matters

✔ Common visualizations:

Line chart → trends over time
Bar/Column chart → comparisons
Pie/Donut chart → proportions
Scatter chart → relationships/correlation
Map → geographic data
Card → single metric
Gauge → progress toward target
Table → detailed records
Matrix → grouped summaries

✔ Interactive feature:

Slicers → filtering

✔ Exam tips:
👉 Trend over time = line chart
👉 Category comparison = bar chart
👉 Relationship between values = scatter chart
👉 Single KPI = card
👉 Geographic data = map

Go to the DP-900 Exam Prep Hub main page.

Analytics, Business Intelligence, Data Modeling, DP-900, Microsoft Certification, Power BI, Power Query May 10, 2026

Describe features of data models in Power BI (DP-900 Exam Prep)

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe data visualization in Microsoft Power BI
      --> Describe features of data models in Power BI

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

A data model is the foundation of any effective report in Microsoft Power BI. It defines how data is structured, related, and calculated, enabling efficient analysis and meaningful visualizations.

For the DP-900 exam, you should understand how data models work, their key components, and best practices.

What Is a Data Model in Power BI?

A data model is a logical representation of data that includes:

Tables
Relationships
Calculations

It allows Power BI to:

Combine data from multiple sources
Enable filtering and aggregation
Support interactive reporting

Key Features of Power BI Data Models

1. Tables

Data models consist of one or more tables, which can come from:

Databases
Files (Excel, CSV)
Cloud sources

✔ Tables contain rows (records) and columns (fields)

2. Relationships

Relationships define how tables are connected.

Types of Relationships

One-to-many (1:*) → Most common
Many-to-one (*:1)
Many-to-many (:)

Key Concepts

Primary key → Unique identifier in one table
Foreign key → Reference in another table

✔ Relationships enable filtering across tables

3. Schema Design (Star Schema)

Power BI models commonly follow a star schema:

Fact tables → Contain measurable data (e.g., sales)
Dimension tables → Contain descriptive data (e.g., customer, product)

✔ This structure improves performance and usability

4. Measures and Calculated Columns

Power BI uses DAX (Data Analysis Expressions) for calculations.

Measures

Calculated at query time
Used in aggregations (e.g., SUM, AVERAGE)

Calculated Columns

Computed during data load
Stored in the model

✔ Measures are preferred for performance

5. Data Types

Each column has a defined data type:

Text
Number
Date/Time
Boolean

✔ Correct data types ensure accurate calculations and visuals

6. Hierarchies

Hierarchies allow users to drill down into data.

Example

Year → Quarter → Month → Day

✔ Used for interactive reporting and exploration

7. Filtering and Cross-Filtering

Relationships enable:

Filter propagation between tables
Cross-filtering in visuals

✔ Example:
Selecting a product filters related sales data

8. Data Granularity

Granularity refers to the level of detail in data.

Fine-grained → detailed (e.g., individual transactions)
Coarse-grained → summarized (e.g., monthly totals)

✔ Consistent granularity is important for accurate analysis

9. Model Optimization

Well-designed models:

Use fewer tables when possible
Avoid unnecessary columns
Use measures instead of calculated columns
Follow star schema design

✔ Improves performance and usability

10. Relationships Direction (Filter Direction)

Relationships can filter:

Single direction (default, recommended)
Both directions (used cautiously)

✔ Incorrect settings can lead to ambiguous results

Typical Data Modeling Workflow in Power BI

Load data into Power BI
Clean and transform data (Power Query)
Define relationships
Create measures and calculations
Build reports and visuals

Why This Matters for DP-900

On the exam, you may be asked to:

Identify components of a data model
Understand relationships and keys
Differentiate between measures and calculated columns
Recognize star schema design
Understand filtering behavior

Summary — Exam-Relevant Takeaways

✔ A data model includes:

Tables
Relationships
Calculations

✔ Key features:

Relationships (1:*, :)
Star schema (fact + dimension tables)
Measures vs calculated columns
Hierarchies and filtering

✔ Best practices:

Use star schema
Prefer measures over calculated columns
Maintain consistent granularity

✔ Exam tips:
👉 Fact table = metrics (numbers)
👉 Dimension table = descriptive attributes
👉 Measure = dynamic calculation
👉 Calculated column = stored value

Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.

Analytics, Business Intelligence Platform, DP-900, Microsoft Certification, Power BI, Power Query May 10, 2026

Practice Questions: Describe features of data models in Power BI (DP-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of a data model in Power BI?

A. To store raw files
B. To define relationships and enable data analysis
C. To manage network connections
D. To create dashboards only

✅ Answer: B

Explanation:
A data model organizes data and defines relationships and calculations for analysis.

Question 2

Which component connects tables together in a Power BI data model?

A. Measures
B. Relationships
C. Dashboards
D. Queries

✅ Answer: B

Explanation:
Relationships define how tables interact and allow filtering across them.

Question 3

Which type of relationship is MOST common in Power BI models?

A. Many-to-many
B. One-to-many
C. One-to-one
D. No relationship

✅ Answer: B

Explanation:
The one-to-many (1:*) relationship is the most common in analytical models.

Question 4

In a star schema, which table typically contains numeric values used for analysis?

A. Dimension table
B. Lookup table
C. Fact table
D. Bridge table

✅ Answer: C

Explanation:
Fact tables store measurable data (e.g., sales, revenue).

Question 5

What is the role of a dimension table in a data model?

A. Store raw transaction data
B. Store aggregated values only
C. Provide descriptive attributes for filtering and grouping
D. Execute calculations

✅ Answer: C

Explanation:
Dimension tables contain descriptive data like customer or product details.

Question 6

Which type of calculation is evaluated at query time in Power BI?

A. Calculated column
B. Measure
C. Table relationship
D. Data type

✅ Answer: B

Explanation:
Measures are calculated dynamically during query execution.

Question 7

Which language is used to create measures and calculated columns in Power BI?

A. SQL
B. Python
C. DAX
D. Java

✅ Answer: C

Explanation:
DAX (Data Analysis Expressions) is used for calculations in Power BI.

Question 8

What is the benefit of using a star schema in Power BI?

A. Increased data duplication
B. Simplified relationships and improved performance
C. Elimination of fact tables
D. Reduced data types

✅ Answer: B

Explanation:
Star schema improves performance and usability by simplifying relationships.

Question 9

What happens when you create a relationship between two tables?

A. Data is duplicated
B. Tables are merged into one
C. Filters can propagate between tables
D. Data types are changed

✅ Answer: C

Explanation:
Relationships allow filtering across related tables.

Question 10

Which feature allows users to drill down through levels such as Year → Month → Day?

A. Measures
B. Hierarchies
C. Relationships
D. Dashboards

✅ Answer: B

Explanation:
Hierarchies enable drill-down analysis in reports.

✅ Quick Exam Takeaways

✔ Data model components:

Tables
Relationships
Measures & calculated columns

✔ Key concepts:

Fact table → numeric data
Dimension table → descriptive data
Relationships → connect tables

✔ Calculations:

Measures → dynamic
Calculated columns → stored

✔ Design best practice:

Use star schema

✔ Exam tip:
👉 Measure = calculated at query time
👉 Calculated column = stored in table
👉 Fact = numbers, Dimension = descriptions

Go to the DP-900 Exam Prep Hub main page.

Analytics, Business Intelligence, DP-900, Microsoft Certification, Power BI, Power Query May 10, 2026

Identify capabilities of Power BI (DP-900 Exam Prep)

This post is a part of the DP-900: Microsoft Azure Data Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Describe an analytics workload (25–30%)
   --> Describe data visualization in Microsoft Power BI
      --> Identify capabilities of Power BI

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Microsoft Power BI is Microsoft’s business intelligence (BI) and data visualization platform. It enables users to connect to data, transform it, and create interactive reports and dashboards for data-driven decision-making.

For the DP-900 exam, you should understand what Power BI can do, its core components, and its role in an analytics solution.

What Is Power BI?

Power BI is a self-service and enterprise BI tool that allows users to:

Connect to multiple data sources
Transform and model data
Create visualizations and reports
Share insights across an organization

Core Capabilities of Power BI

1. Data Connectivity

Power BI can connect to a wide range of data sources:

Cloud services (Azure, SaaS apps)
Databases (SQL Server, Azure SQL)
Files (Excel, CSV)
Streaming data sources

✔ Supports both import and direct query modes

2. Data Transformation (Power Query)

Power BI includes Power Query, a tool for:

Cleaning data
Shaping and transforming data
Merging and filtering datasets

✔ Uses a visual interface (no coding required, though M language is available)

3. Data Modeling

Power BI enables users to create data models by:

Defining relationships between tables
Creating calculated columns and measures
Using DAX (Data Analysis Expressions)

✔ Supports star schema design (common in analytics)

4. Data Visualization

Power BI provides a rich set of visualizations:

Charts (bar, line, pie, etc.)
Tables and matrices
Maps and geographic visuals
KPIs and gauges

✔ Visuals are interactive and dynamic

5. Reports

A report in Power BI:

Is a collection of visualizations
Typically spans multiple pages
Allows filtering, slicing, and drill-down

✔ Built in Power BI Desktop and published to the cloud

6. Dashboards

A dashboard:

Is a single-page view of key metrics
Displays pinned visuals from reports
Provides a high-level overview

✔ Used for quick insights and monitoring

7. Data Refresh

Power BI supports:

Scheduled refresh (periodic updates)
Real-time/streaming data updates

✔ Ensures reports reflect current data

8. Sharing and Collaboration

Power BI enables users to:

Publish reports to the Power BI Service
Share dashboards with others
Collaborate across teams

✔ Integrates with Microsoft 365 (Teams, SharePoint)

9. Security

Power BI provides:

Row-Level Security (RLS)
Data access controls
Integration with Azure Active Directory

✔ Ensures users only see authorized data

10. Integration with Azure and Microsoft Ecosystem

Power BI integrates with:

Azure Synapse Analytics
Azure Data Lake Storage
Microsoft Fabric
Excel and other Microsoft tools

✔ Plays a key role in end-to-end analytics solutions

Power BI Components

Power BI Desktop

Authoring tool for reports
Installed on a local machine

Power BI Service

Cloud-based platform
Used for sharing and collaboration

Power BI Mobile

View dashboards and reports on mobile devices

Typical Analytics Workflow with Power BI

Connect to data sources
Transform data (Power Query)
Model data (relationships, DAX)
Create visualizations
Publish reports
Share dashboards

Why This Matters for DP-900

On the exam, you may be asked to:

Identify Power BI capabilities
Differentiate between reports and dashboards
Understand data connectivity and refresh options
Recognize Power BI’s role in analytics solutions

Summary — Exam-Relevant Takeaways

✔ Power BI is used for:

Data visualization
Reporting
Business intelligence

✔ Key capabilities:

Data connectivity
Data transformation (Power Query)
Data modeling (relationships, DAX)
Interactive visualizations
Sharing and collaboration

✔ Key components:

Power BI Desktop → report creation
Power BI Service → sharing
Dashboards → single-page overview
Reports → multi-page detailed analysis

✔ Exam tips:
👉 Report = multi-page, detailed
👉 Dashboard = single-page, summary
👉 Power Query = data transformation
👉 DAX = calculations and measures

Go to the Practice Exam Questions for this topic.

Go to the DP-900 Exam Prep Hub main page.

Analytics, Business Intelligence, DP-900, Microsoft Certification, Power BI, Power Query May 10, 2026

Practice Questions: Identify capabilities of Power BI (DP-900 Exam Prep)

Practice Questions

Question 1

What is the primary purpose of Microsoft Power BI?

A. Managing databases
B. Running virtual machines
C. Creating reports and visualizations from data
D. Developing applications

✅ Answer: C

Explanation:
Power BI is a business intelligence tool used to create reports, dashboards, and visualizations.

Question 2

Which Power BI component is used to create reports?

A. Power BI Service
B. Power BI Mobile
C. Power BI Desktop
D. Azure Portal

✅ Answer: C

Explanation:
Power BI Desktop is the primary tool for building reports and data models.

Question 3

What is the main difference between a report and a dashboard in Power BI?

A. Reports are single-page, dashboards are multi-page
B. Reports are multi-page, dashboards are single-page
C. Reports are only for developers
D. Dashboards cannot contain visuals

✅ Answer: B

Explanation:
Reports are multi-page and detailed, while dashboards are single-page summaries.

Question 4

Which feature in Power BI is used to clean and transform data?

A. DAX
B. Power Query
C. Power Pivot
D. Azure Data Factory

✅ Answer: B

Explanation:
Power Query is used for data transformation and preparation.

Question 5

Which language is used in Power BI for creating calculations and measures?

A. SQL
B. Python
C. DAX
D. Java

✅ Answer: C

Explanation:
DAX (Data Analysis Expressions) is used for calculations and measures.

Question 6

Which Power BI feature allows users to restrict data access to specific rows?

A. Data refresh
B. Row-Level Security (RLS)
C. Power Query
D. Dashboards

✅ Answer: B

Explanation:
Row-Level Security (RLS) ensures users only see data they are authorized to access.

Question 7

Which of the following is a key capability of Power BI?

A. Running operating systems
B. Hosting web applications
C. Connecting to multiple data sources
D. Managing network traffic

✅ Answer: C

Explanation:
Power BI can connect to many different data sources, including databases, files, and cloud services.

Question 8

Where are Power BI reports typically published for sharing and collaboration?

A. Power BI Desktop
B. Power BI Service
C. Azure Virtual Machines
D. SQL Server

✅ Answer: B

Explanation:
Reports are published to the Power BI Service for sharing and collaboration.

Question 9

Which capability allows Power BI to display near real-time data?

A. Scheduled refresh only
B. Streaming datasets
C. Static reports
D. Data export

✅ Answer: B

Explanation:
Streaming datasets enable real-time or near real-time updates.

Question 10

What is the purpose of a Power BI dashboard?

A. To store raw data
B. To create data pipelines
C. To provide a single-page view of key metrics
D. To manage user accounts

✅ Answer: C

Explanation:
Dashboards provide a high-level, single-page summary of important data.

✅ Quick Exam Takeaways

✔ Power BI is used for:

Data visualization
Reporting
Business intelligence

✔ Key features:

Power Query → data transformation
DAX → calculations
Reports → multi-page
Dashboards → single-page

✔ Components:

Power BI Desktop → build reports
Power BI Service → share and collaborate

✔ Security:

Row-Level Security (RLS)

✔ Exam tip:
👉 Transform data → Power Query
👉 Create calculations → DAX
👉 Share reports → Power BI Service

Go to the DP-900 Exam Prep Hub main page.