Tag: azure

AI, AI-900, Artificial Intelligence (AI), Computer Vision, Data Education & Training, Microsoft Certification

Identify Computer Vision Workloads (AI-900 Exam Prep)

Overview

Computer vision is a branch of Artificial Intelligence (AI) that enables machines to interpret, analyze, and understand visual information such as images and videos. In the context of the AI-900: Microsoft Azure AI Fundamentals exam, you are not expected to build complex models or write code. Instead, the focus is on recognizing computer vision workloads, understanding what problems they solve, and knowing which Azure AI services are appropriate for each scenario.

This topic falls under:

  • Describe Artificial Intelligence workloads and considerations (15–20%)
    • Identify features of common AI workloads

A strong conceptual understanding here will help you confidently answer many scenario-based exam questions.

What Is a Computer Vision Workload?

A computer vision workload involves extracting meaningful insights from visual data. These workloads allow systems to:

  • Identify objects, people, or text in images
  • Analyze facial features or emotions
  • Understand the content of photos or videos
  • Detect changes, anomalies, or motion

Common inputs include:

  • Images (JPEG, PNG, etc.)
  • Video streams (live or recorded)

Common outputs include:

  • Labels or tags
  • Bounding boxes around detected objects
  • Extracted text
  • Descriptions of image content

Common Computer Vision Use Cases

On the AI-900 exam, computer vision workloads are usually presented as real-world scenarios. Below are the most common ones you should recognize.

Image Classification

What it does: Assigns a category or label to an image.

Example scenarios:

  • Determining whether an image contains a cat, dog, or bird
  • Classifying products in an online store
  • Identifying whether a photo shows food, people, or scenery

Key idea: The entire image is classified as one or more categories.

Object Detection

What it does: Detects and locates multiple objects within an image.

Example scenarios:

  • Detecting cars, pedestrians, and traffic signs in street images
  • Counting people in a room
  • Identifying damaged items in a warehouse

Key idea: Unlike classification, object detection identifies where objects appear using bounding boxes.

Face Detection and Facial Analysis

What it does: Detects human faces and analyzes facial attributes.

Example scenarios:

  • Detecting whether a face is present in an image
  • Estimating age or emotion
  • Identifying facial landmarks (eyes, nose, mouth)

Important exam note:

  • AI-900 focuses on face detection and analysis, not facial recognition for identity verification.
  • Be aware of ethical and privacy considerations when working with facial data.

Optical Character Recognition (OCR)

What it does: Extracts printed or handwritten text from images and documents.

Example scenarios:

  • Reading text from scanned documents
  • Extracting information from receipts or invoices
  • Recognizing license plate numbers

Key idea: OCR turns unstructured visual text into machine-readable text.

Image Description and Tagging

What it does: Generates descriptive text or tags that summarize image content.

Example scenarios:

  • Automatically tagging photos in a digital library
  • Creating alt text for accessibility
  • Generating captions for images

Key idea: This workload focuses on understanding the overall context of an image rather than specific objects.

Video Analysis

What it does: Analyzes video content frame by frame.

Example scenarios:

  • Detecting motion or anomalies in security footage
  • Tracking objects over time
  • Summarizing video content

Key idea: Video analysis extends image analysis across time, not just a single frame.

Azure Services Commonly Associated with Computer Vision

For the AI-900 exam, you should recognize which Azure AI services support computer vision workloads at a high level.

Azure AI Vision

Supports:

  • Image analysis
  • Object detection
  • OCR
  • Face detection
  • Image tagging and description

This is the most commonly referenced service for computer vision scenarios on the exam.

Azure AI Custom Vision

Supports:

  • Custom image classification
  • Custom object detection

Used when prebuilt models are not sufficient and you need to train a model using your own images.

Azure AI Video Indexer

Supports:

  • Video analysis
  • Object, face, and scene detection in videos

Typically appears in scenarios involving video content.

How Computer Vision Differs from Other AI Workloads

Understanding what is not computer vision is just as important on the exam.

AI Workload TypeFocus Area
Computer VisionImages and videos
Natural Language ProcessingText and speech
Speech AIAudio and voice
Anomaly DetectionPatterns in numerical or time-series data

Exam tip: If the input data is visual (images or video), you are almost certainly dealing with a computer vision workload.

Responsible AI Considerations

Microsoft emphasizes responsible AI, and AI-900 includes high-level awareness of these principles.

For computer vision workloads, key considerations include:

  • Privacy and consent when capturing images or video
  • Avoiding bias in facial analysis
  • Transparency in how visual data is collected and used

You will not be tested on implementation details, but you may see conceptual questions about ethical use.

Exam Tips for Identifying Computer Vision Workloads

  • Focus on keywords like image, photo, video, camera, scanned document
  • Look for actions such as detect, recognize, classify, extract text
  • Match the scenario to the simplest appropriate workload
  • Remember: AI-900 tests understanding, not coding

Summary

To succeed on the AI-900 exam, you should be able to:

  • Recognize when a problem is a computer vision workload
  • Identify common use cases such as image classification, object detection, and OCR
  • Understand which Azure AI services are commonly used
  • Distinguish computer vision from other AI workloads

Mastering this topic will give you a strong foundation for many questions in the Describe Artificial Intelligence workloads and considerations domain.

Go to the Practice Exam Questions for this topic.

Go to the PL-300 Exam Prep Hub main page.

Analytics, BI Administration, Business Intelligence, Business Intelligence (BI) Development, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Power BI, Power Query

Implement Incremental Refresh for Semantic Models

 
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 Incremental Refresh for Semantic Models

Overview

Incremental refresh is a key optimization technique for enterprise-scale semantic models in Microsoft Fabric and Power BI. Instead of fully refreshing all data during each refresh cycle, incremental refresh allows you to refresh only new or changed data, significantly improving refresh performance, reducing resource consumption, and enabling scalability for large datasets.

In the DP-600 exam, this topic appears under Optimize enterprise-scale semantic models and focuses on when, why, and how to configure incremental refresh correctly.

What Is Incremental Refresh?

Incremental refresh is a feature for Import mode and Hybrid (Import + DirectQuery) semantic models that:

  • Partitions data based on date/time columns
  • Refreshes only a recent portion of data
  • Retains historical data without reprocessing it
  • Optionally supports real-time data using DirectQuery

Incremental refresh is not applicable to:

  • Direct Lake–only semantic models
  • Pure DirectQuery models

Key Benefits

Incremental refresh provides several enterprise-level advantages:

  • Faster refresh times for large datasets
  • Reduced memory and CPU usage
  • Improved reliability of scheduled refreshes
  • Better scalability for growing fact tables
  • Enables near-real-time analytics when combined with DirectQuery

Core Configuration Components

1. Date/Time Column Requirement

Incremental refresh requires a column that:

  • Is of type Date, DateTime, or DateTimeZone
  • Represents a monotonically increasing timeline (for example, OrderDate or TransactionDate)

This column is used to define data partitions.

2. RangeStart and RangeEnd Parameters

Incremental refresh relies on two Power Query parameters:

  • RangeStart – Beginning of the refresh window
  • RangeEnd – End of the refresh window

These parameters:

  • Must be of type Date/Time
  • Are used in a filter step in Power Query
  • Are evaluated dynamically during refresh

Exam tip: These parameters are required, not optional.

3. Refresh and Storage Policies

When configuring incremental refresh, you define two key time windows:

PolicyPurpose
Store rows from the pastDefines how much historical data is retained
Refresh rows from the pastDefines how much recent data is refreshed

Example:

  • Store data for 5 years
  • Refresh data from the last 7 days

Only the refresh window is reprocessed during each refresh.

4. Optional: Detect Data Changes

Incremental refresh can optionally use a change detection column (for example, LastModifiedDate):

  • Only refreshes partitions where data has changed
  • Reduces unnecessary refresh operations
  • Column must be reliably updated when records change

This is especially useful for slowly changing dimensions.

Incremental Refresh with Real-Time Data (Hybrid Tables)

Incremental refresh can be combined with DirectQuery to support real-time data:

  • Historical data → Import mode
  • Recent data → DirectQuery

This configuration:

  • Uses the “Get the latest data in real time” option
  • Is commonly referred to as a Hybrid table
  • Balances performance with freshness

Deployment and Execution Behavior

  • Incremental refresh is defined in Power BI Desktop
  • Partitions are created only after publishing
  • Refresh execution happens in the Fabric service
  • Desktop refresh does not create partitions

Exam tip: Many questions test the difference between design-time configuration and service-side execution.

Limitations and Considerations

  • Requires Import or Hybrid mode
  • Date column must exist in the fact table
  • Cannot be configured directly in Fabric service
  • Schema changes may require full refresh
  • Partition count should be managed to avoid excessive overhead

Common DP-600 Exam Scenarios

You may be asked to:

  • Choose incremental refresh to solve long refresh times
  • Identify missing requirements (RangeStart/RangeEnd)
  • Decide between full refresh vs incremental refresh
  • Configure refresh windows for historical vs recent data
  • Combine incremental refresh with real-time analytics

When to Use Incremental Refresh (Exam Heuristic)

Choose incremental refresh when:

  • Fact tables are large and growing
  • Only recent data changes
  • Full refresh times are too long
  • Import mode is required for performance

Avoid it when:

  • Data volume is small
  • Real-time access is required for all data
  • Using Direct Lake–only models

Exam Tips

For DP-600, remember:

  • RangeStart / RangeEnd are mandatory
  • Incremental refresh = Import or Hybrid
  • Partitions are service-side
  • Refresh window ≠ storage window
  • Hybrid tables enable real-time + performance

Summary

Incremental refresh is a foundational optimization technique for large semantic models in Microsoft Fabric. For the DP-600 exam, focus on:

  • Required parameters (RangeStart, RangeEnd)
  • Refresh vs storage windows
  • Import and Hybrid model compatibility
  • Real-time and change detection scenarios
  • Service-side execution behavior

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

Question 1

You have a large fact table with 5 years of historical data. Only the most recent data changes daily. Which feature should you implement to reduce refresh time?

A. DirectQuery mode
B. Incremental refresh
C. Calculated tables
D. Composite models

✅ Correct Answer: B

Explanation:
Incremental refresh is designed to refresh only recent data while retaining historical partitions, significantly improving refresh performance for large datasets.

Question 2

Which two Power Query parameters are required to configure incremental refresh?

A. StartDate and EndDate
B. MinDate and MaxDate
C. RangeStart and RangeEnd
D. RefreshStart and RefreshEnd

✅ Correct Answer: C

Explanation:
Incremental refresh requires RangeStart and RangeEnd parameters of type Date/Time to define partition boundaries.

Question 3

Where are incremental refresh partitions actually created?

A. Power BI Desktop during data load
B. Fabric Data Factory
C. Microsoft Fabric service after publishing
D. SQL endpoint

✅ Correct Answer: C

Explanation:
Partitions are created and managed only in the Fabric service after the model is published. Desktop refresh does not create partitions.

Question 4

Which storage mode is required to use incremental refresh?

A. DirectQuery only
B. Direct Lake only
C. Import or Hybrid
D. Dual only

✅ Correct Answer: C

Explanation:
Incremental refresh works with Import mode and Hybrid tables. It is not supported for DirectQuery-only or Direct Lake–only models.

Question 5

You configure incremental refresh to store 5 years of data and refresh the last 7 days. What happens during a scheduled refresh?

A. All data is fully refreshed
B. Only the last 7 days are refreshed
C. Only the last year is refreshed
D. Only new rows are loaded

✅ Correct Answer: B

Explanation:
The refresh window defines how much data is reprocessed. Historical partitions outside that window are retained without refresh.

Question 6

Which column type is required for incremental refresh filtering?

A. Text
B. Integer
C. Boolean
D. Date/DateTime

✅ Correct Answer: D

Explanation:
Incremental refresh requires a Date, DateTime, or DateTimeZone column to define time-based partitions.

Question 7

What is the purpose of the Detect data changes option?

A. To refresh all partitions automatically
B. To detect schema changes
C. To refresh only partitions where data has changed
D. To enable real-time DirectQuery

✅ Correct Answer: C

Explanation:
Detect data changes uses a change-tracking column (e.g., LastModifiedDate) to avoid refreshing partitions when no data has changed.

Question 8

Which scenario best fits a Hybrid incremental refresh configuration?

A. All data must be queried in real time
B. Small dataset refreshed once per day
C. Historical data rarely changes, but recent data must be real time
D. Streaming data only

✅ Correct Answer: C

Explanation:
Hybrid tables combine Import for historical data and DirectQuery for recent data, providing real-time access where needed.

Question 9

What happens if the date column used for incremental refresh contains null values?

A. Incremental refresh is automatically disabled
B. Only historical partitions fail
C. Refresh may fail or produce incorrect partitions
D. Null values are ignored safely

✅ Correct Answer: C

Explanation:
The date column must be reliable. Null or invalid values can break partition logic and cause refresh failures.

Question 10

When should you avoid using incremental refresh?

A. When the dataset is large
B. When only recent data changes
C. When using Direct Lake–only semantic models
D. When refresh duration is long

✅ Correct Answer: C

Explanation:
Incremental refresh is not supported for Direct Lake–only models, as Direct Lake handles freshness differently through OneLake access.

Analytics, Data Governance, Data Integration, Data Warehousing, Microsoft Fabric, Microsoft OneLake, Power BI

Understanding Microsoft Fabric Shortcuts

Microsoft Fabric is a central platform for data and analytics, and one of its powerful features that supports it being an all-in-one platform is Shortcuts. Shortcuts provide a simple way to unify data across multiple locations without duplicating or moving it. This is a big deal because it saves a LOT of time and effort that is usually involved in moving data around.

What Are Shortcuts?

Shortcuts are references (or “pointers”) to data that resides in another storage location. Instead of copying the data into Fabric, a shortcut lets you access and query it as if it were stored locally.

This is especially valuable in today’s data landscape, where data often spans OneLake, Azure Data Lake Storage (ADLS), Amazon S3, or other environments.

Types of Shortcuts

There are 2 types of shortcuts: table shortcuts and file shortcuts

  1. Table Shortcuts
    • Point to existing tables in other Fabric workspaces or external sources.
    • Allow you to query and analyze the table without physically moving it.
  2. File Shortcuts
    • Point to files (e.g., Parquet, CSV, Delta Lake) stored in OneLake or other supported storage systems.
    • Useful for scenarios where files are your system of record, but you want to use them in Fabric experiences like Power BI, Data Engineering, or Data Science.

Benefits of Shortcuts

Shortcuts is a really useful feature, and here are some of its benefits:

  • No Data Duplication: Saves storage costs and avoids data sprawl.
  • Single Source of Truth: Data stays in its original location while being usable across Fabric.
  • Speed and Efficiency: Query and analyze external data in place, without lengthy ETL processes.
  • Flexibility: Works across different storage platforms and Fabric workspaces.

How and Where Shortcuts Can Be Created

  • In OneLake: You can create shortcuts directly in OneLake to link to data from ADLS Gen2, Amazon S3, or other OneLake workspaces.
  • In Fabric Experiences: Whether working in Data Engineering, Data Science, Real-Time Analytics, or Power BI, shortcuts can be created in lakehouses or KQL (Kusto Query Language) databases, and you can use them directly as data in OneLake. Any Fabric service will be able to use them without copying data from the data source.
  • In Workspaces: Shortcuts make it possible to connect across lakehouses stored in different workspaces, breaking down silos within an organization. The shortcuts can be generated from a lakehouse, warehouse, or KQL database.
  • Note that warehouses do not support the creation of shortcuts. However, you can query data stored within other warehouses and lakehouses.

How Shortcuts Can Be Used

  • Cross-Workspace Data Access: Analysts can query data in another team’s workspace without requesting a copy.
  • Data Virtualization: Data scientists can work with files stored in ADLS without having to move them into Fabric.
  • BI and Reporting: Power BI models can use shortcuts to reference external files or tables, enabling consistent reporting without duplication.
  • ETL Simplification: Instead of moving raw files into Fabric, engineers can create shortcuts and build transformations directly on the source.

Common Scenarios

  • A finance team wants to build Power BI reports on data stored by the operations team without moving the data.
  • A data scientist needs access to parquet files in Amazon S3 but prefers to analyze them within Fabric.
  • A company with multiple Fabric workspaces wants to centralize access to shared reference data (like customer or product master data) without replication.

In summary: Microsoft Fabric Shortcuts simplify data access across locations and workspaces. Whether table-based or file-based, they allow organizations to unify data without duplication, streamline analytics, and improve collaboration.

Here is a link to the Microsoft Learn OneLake documentation about Shortcuts. From there you will be able to explore all the Shortcut topics shown in the image below:

Thanks for reading! I hope you found this information useful.