Category: Power BI

Business Intelligence, Data Cleaning, Data Quality Assurance, Microsoft Certification, PL-300, Power BI January 17, 2026

Resolve inconsistencies, unexpected or null values, and data quality issues (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Prepare the data (25–30%) 
  --> Profile and clean the data 
    --> Resolve inconsistencies, unexpected or null values, and data quality issues

Note that there are 10 practice questions (with answers and explanations) at the end of 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's main page.

High-quality data is essential for accurate analysis and trustworthy reports. In the PL-300 exam, Microsoft expects candidates to understand how to identify and resolve common data quality problems using Power Query before data is loaded into the model.

This section focuses on handling inconsistencies, unexpected values, nulls, and errors—all of which can negatively impact calculations, relationships, and visuals if left unresolved.

Why This Topic Matters for the Exam

From an exam perspective, this topic tests your ability to:

Diagnose data quality problems using profiling tools
Choose the correct transformation to fix an issue
Understand when to remove, replace, or transform data
Prevent downstream modeling and reporting issues

Most questions are scenario-based, asking what action you should take to fix a specific data issue.

Common Data Quality Issues You Must Recognize

1. Null (Blank) Values

Nulls represent missing or unknown data and can cause:

Incorrect aggregations
Broken relationships
Visuals that behave unexpectedly

Common causes:

Incomplete source data
Left joins with no matching rows
Data entry gaps

2. Unexpected or Invalid Values

These include:

Negative values where only positives make sense
Text values in numeric columns
Dates outside expected ranges
Misspelled or inconsistent category names

3. Inconsistent Data

Inconsistencies often appear as:

Mixed casing (USA vs usa)
Trailing or leading spaces
Multiple spellings for the same value
Different date or number formats

4. Error Values

Errors usually occur when:

Converting data types
Performing calculations
Parsing malformed data

Examples include:

Conversion failed
Divide by zero
Invalid date format

Identifying Data Quality Issues in Power Query

Power Query provides built-in data profiling tools to quickly detect problems:

Column Quality

Shows percentages of Valid, Error, and Empty values
Ideal for spotting nulls and errors

Column Distribution

Displays value frequency and distinct counts
Helps identify unexpected or inconsistent values

Column Profile

Provides min, max, average, and other statistics
Useful for detecting outliers and invalid ranges

Exam Tip: Profiling tools only analyze a sample by default. You may need to enable “Column profiling based on entire dataset” for accuracy.

Techniques to Resolve Null Values

Remove Rows

Used when nulls make a record unusable
Common for missing primary keys or required fields

Replace Values

Replace nulls with:
- 0 (for numeric measures)
- “Unknown” or “Not Provided” (for text)
- A default date

Fill Down / Fill Up

Used for hierarchical or grouped data
Common in spreadsheets with merged cells

Exam Insight: Replacing nulls should be a business-justified decision, not automatic.

Resolving Inconsistencies

Standardizing Text

Use Transform → Format:
- Uppercase
- Lowercase
- Capitalize Each Word

Trimming and Cleaning

Trim removes leading/trailing spaces
Clean removes non-printable characters

Replacing Values

Normalize spelling differences (e.g., “US”, “USA”, “United States”)

Handling Unexpected or Invalid Values

Filtering

Remove values outside acceptable ranges
Exclude invalid categories

Conditional Columns

Create logic to flag or correct invalid data
Example: Replace negative sales with null or zero

Data Type Corrections

Ensure columns use appropriate data types
Prevents aggregation and calculation errors later

Fixing Error Values

Replace Errors

Replace with null or a default value

Remove Errors

Used when rows are unreliable

Fix the Root Cause

Change transformation order
Adjust data type conversion
Clean data before applying calculations

Exam Tip: Microsoft often tests whether you know why an error occurs, not just how to remove it.

Best Practices for PL-300 Candidates

Always profile before transforming
Fix issues in Power Query, not DAX, when possible
Understand the impact of removing vs replacing data
Keep transformations repeatable and documented
Prefer clean data models over complex report logic

Key Takeaways for the Exam

You should be able to:

Identify different types of data quality issues
Choose the correct Power Query tool to resolve them
Understand the downstream impact on models and visuals
Interpret profiling results correctly

Mastering this topic ensures cleaner datasets, better models, and fewer surprises during analysis—exactly what the PL-300 exam is designed to validate.

Practice Questions

Go to the Practice Exam Questions for this topic.

Analytics, Business Intelligence, Data Cleaning, Data Integration, Data Modeling, Microsoft Certification, PL-300, Power BI January 17, 2026January 17, 2026

Evaluate Data including Data Statistics & Column Properties (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Prepare the data (25–30%) 
    --> Profile and clean the data 
        --> Evaluate data, including data statistics and column properties

Note that there are 10 practice questions (with answers and explanations) at the end of 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.

Before cleaning, transforming, or modeling data, a Power BI Data Analyst must first evaluate the quality and structure of the data. The PL-300 exam tests your ability to profile data, interpret data statistics, and understand column properties to identify issues such as missing values, incorrect data types, outliers, and inconsistent formats.

This topic lives under Profile and clean the data because effective data preparation starts with understanding what the data looks like and how it behaves.

What Does “Evaluate Data” Mean in Power BI?

Evaluating data means using Power BI (specifically Power Query) to:

Understand data distribution and completeness
Identify data quality issues
Verify correct data types and formats
Decide what cleaning or transformation steps are required

Rather than guessing, Power BI provides built-in profiling tools that summarize data characteristics automatically.

Data Profiling Tools in Power Query

Power BI includes several profiling features that appear in the Power Query Editor, primarily within the View tab.

Key Data Profiling Options

Column quality
Column distribution
Column profile

These tools help you quickly assess whether a column is usable, trustworthy, and correctly defined.

Column Quality

Column quality provides a high-level overview of data completeness and validity.

It visually displays:

Valid values
Error values
Empty (null) values

Why Column Quality Matters

Quickly highlights missing or broken data
Helps determine whether rows should be filtered, fixed, or removed
Useful for early detection of refresh or ingestion issues

📌 Exam insight:
Questions often test whether you can identify which tool reveals missing or invalid values—column quality is the answer.

Column Distribution

Column distribution shows how values are spread across a column.

It provides:

Frequency of values
Distinct vs unique counts
A histogram-style visualization (for numeric fields)

Common Uses

Spotting unexpected duplicates
Identifying skewed data
Detecting outliers
Validating categorical values

📌 Exam insight:
Column distribution is used to understand value frequency, not just nulls or errors.

Column Profile

Column profile gives the most detailed statistical view of a column.

Depending on the data type, it may include:

Minimum and maximum values
Average
Standard deviation
Count and distinct count
Null count

Typical Use Cases

Verifying numeric ranges (e.g., negative values where none should exist)
Checking date ranges
Understanding overall data shape before modeling

📌 Exam insight:
Column profile helps validate statistical characteristics, not formatting or naming.

Understanding Column Properties

Beyond statistics, Power BI also evaluates column properties, which affect how data behaves in the model and visuals.

Key Column Properties to Evaluate

Data Type

Examples:

Whole number
Decimal number
Text
Date / DateTime
Boolean

Incorrect data types can:

Break visuals
Prevent aggregations
Cause relationship issues

📌 Exam tip:
Always verify data types before applying transformations or creating measures.

Format

Controls how values appear (e.g., currency, percentage, date format).

Affects display, not calculation logic
Often adjusted after validating data type

Default Summarization

Determines how numeric columns aggregate in visuals:

Sum
Average
Count
Do not summarize

📌 Exam insight:
Default summarization is evaluated when deciding how columns behave in visuals—not during Power Query transformations.

Column Name & Description

Clear names improve usability
Descriptions help report consumers understand the data

While not deeply technical, the exam may include best-practice questions around data clarity and usability.

Evaluating Data at the Right Stage

Most evaluation tasks occur in Power Query, before data is loaded into the model.

Why?

Faster detection of issues
Prevents poor-quality data from entering the model
Reduces downstream modeling complexity

📌 Key distinction for the exam:

Power Query → data evaluation & cleaning
Model view → relationships & behavior
Report view → visualization

Common Exam Scenarios

You may encounter questions like:

Scenario 1

You need to quickly identify columns with missing or invalid values.

Correct concept: Column quality

Scenario 2

You want to understand how frequently values appear in a categorical column.

Correct concept: Column distribution

Scenario 3

You need to verify numeric ranges and detect outliers.

Correct concept: Column profile

Scenario 4

A numeric column is being treated as text and cannot be aggregated.

Correct concept: Incorrect data type (column property)

Best Practices to Remember

Enable profiling tools early in data preparation
Validate data types before transformations
Use statistics to guide cleaning decisions
Don’t rely on visuals alone to detect data quality issues

Key Exam Takeaways

For the PL-300 exam, remember:

Column quality → valid, error, and null values
Column distribution → frequency and distinct values
Column profile → statistical insights
Column properties affect aggregation, relationships, and visuals
Data evaluation happens primarily in Power Query

Understanding how to interpret what Power BI is telling you about your data is just as important as knowing how to clean it.

Practice Questions

Go to the Practice Exam Questions for this topic.

Business Intelligence, Microsoft Certification, PL-300, Power BI January 17, 2026

Create and Modify Parameters (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Get or connect to data
      --> Create and Modify Parameters

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.

As you prepare for the PL-300: Microsoft Power BI Data Analyst exam, one important skill to master is using parameters in Power Query to build flexible, reusable, and dynamic data connections and transformations. Parameters help you abstract static values—such as server names, file paths, or filter values—and manage them in a centralized way. Microsoft Learn

What Are Parameters in Power BI?

A parameter is a named value that you can use in Power Query to influence how queries run. Instead of hard-coding values directly into your query steps, a parameter lets you declare the value once and reference it throughout your queries so you can:

Change values globally without editing each query step.
Switch between environments (Dev, Test, Prod) easily.
Apply consistent filters or thresholds.
Support dynamic filtering and modular query design.

In many ways, parameters act like variables in the Power Query (M) engine—reusable building blocks you can plug into various parts of your query logic. Microsoft Learn

Where You Use Parameters

Parameters are especially useful when connecting and shaping data:

At the data source level

Define the server, database, folder, or file path.
Change the current environment without rewriting source steps.

In query logic

Filter tables based on parameter values.
Customize operations like date range filters or limit values.

For reusability

Use the same parameter value across multiple queries, reducing maintenance.
Make Power BI solutions easier to support if data locations change. Microsoft Learn

How to Create Parameters

In Power BI Desktop:

Go to Home → Transform data → Transform data to open Power Query Editor.
In the Power Query Editor, choose Manage Parameters → New Parameter.
In the Manage Parameters dialog, provide:
- Name (meaningful identifier for the parameter)
- Description (optional, useful for documentation)
- Required (whether a value must be entered)
- Type (Text, Number, Date, etc.)
- Suggested values (optional: helps users pick from list, default, etc.)
- Current value (the value Power Query uses when the parameter is applied) Microsoft Learn

Once created, the parameter appears in Power Query as its own query and can be referenced in other queries.

Example Uses of Parameters

Here are typical scenarios where parameters are valuable:

1. Dynamic Data Source Connection

If a report needs to point to Development in one workflow and Production in another, you can create parameters for server name and database name, then reference them in the data source step.

This avoids hard-coding connection strings and simplifies environment switches without editing M code. 3Cloud

2. Reusable Filters

Suppose you want to filter a sales table by a minimum sales amount that might change often. Instead of editing the filter step directly each time, you use a parameter called MinSalesThreshold. If business needs change, you update the parameter once, and all queries referencing it update accordingly. Microsoft Learn

3. Parameter-Driven Queries

You can embed a parameter into custom functions or use it directly in the M query logic (e.g., in Table.SelectRows), making your query logic adaptable without editing M code every time. Microsoft Learn

Modifying Parameters

Once a parameter is created:

Return to Manage Parameters in Power Query Editor to change metadata (name, description, type).
Update the Current Value to change how queries refresh with different values.
If you change suggested values, you may also redefine the list of allowed inputs.

Changing a parameter’s current value typically triggers a refresh of any queries that reference it, applying the new logic or sources immediately. Microsoft Learn

Best Practices for Parameters

Use clear names and descriptions
Someone else reviewing your model should immediately understand the purpose of each parameter.

Keep parameter types consistent
Ensure the data type matches how you intend to use it in queries to avoid type mismatch errors.

Leverage suggested values
If a parameter is intended to allow only certain options (e.g., environment names), defining suggested values improves usability and reduces errors.

Document your parameters
Include descriptions so others know what each parameter does and why it exists. Microsoft Learn

How This Appears on the PL-300 Exam

You may see exam scenarios such as:

“You need to allow end users to easily switch data sources without editing M code.”
“Modify a query so that multiple environments use the same report with different connection settings.”
“Filter the data using a parameter and explain how the data refresh behaves.”

To answer confidently:

Know how to create and configure parameters.
Understand how parameters change the behavior of queries and filtering.
Recognize when parameters simplify maintenance and governance.

This skill not only helps you build robust and flexible Power BI solutions but also aligns with the Prepare the data objectives in the PL-300 exam. Microsoft Learn

Practice Questions

Go to the Practice Exam Questions for this topic.

Microsoft Certification, PL-300, Power BI January 17, 2026

Choose Between DirectQuery and Import (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Prepare the data (25–30%) 
    --> Get or connect to data 
        --> Choose Between DirectQuery and Import

Note that there are 10 practice questions (with answers and explanations) at the end of 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.

One of the critical decisions a Power BI Data Analyst makes is how Power BI retrieves and interacts with data. The choice between DirectQuery and Import affects performance, modeling flexibility, scalability, and how data refresh is managed. This topic is core to the Get or connect to data objective and frequently appears in scenario-based exam questions.

What Are Import and DirectQuery?

Before comparing them, it’s important to understand what each mode does:

Import: Loads data from a data source into the Power BI dataset’s in-memory storage. Once imported, Power BI interacts with the data locally and refreshes it periodically.
DirectQuery: Does not store data in Power BI. Instead, it queries the source system in real time whenever the report needs data.

Import vs. DirectQuery – Conceptual Overview

Here’s the high-level conceptual difference:

Aspect	Import	DirectQuery
Where data is stored	In Power BI’s in-memory engine	In the source system
Performance	Very fast (in-memory)	Depends on source performance
Data freshness	Requires scheduled refresh	Always up-to-date at query time
Modeling flexibility	Full	Limited
Query folding benefit	Yes	Limited (source dependent)
Refresh required	Yes	No
Suitable for large tables?	Depends on size	Yes
Depends on gateway?	Only for on-premises sources	Yes for on-premises sources

When to Choose Import

Choose Import when:

🟢 Performance and Interactivity Matter

Because data is loaded into memory, visuals and filters respond very quickly, which is ideal for dashboards and interactive exploration.

🟢 Full Modeling Flexibility Is Needed

Import supports:

Complex DAX
Calculated tables & columns
Relationship editing
Role-level security

This makes it the most flexible and commonly used mode.

🟢 Data Volume is Manageable

For moderately sized datasets that fit comfortably in memory, Import is typically preferred.

Example Scenario

A finance team needs a dashboard that refreshes nightly and delivers fast slicing and dicing of historical sales data.

In this case, Import is the likely choice.

When to Choose DirectQuery

Opt for DirectQuery when:

🔵 Data Must Stay at Source

Some data may be too large or sensitive to copy into Power BI. With DirectQuery, data remains on the original system.

🔵 Real-Time or Near-Real-Time Data is Required

If reports must reflect the most current operational data instantly, DirectQuery ensures up-to-date results.

🔵 Data Volume Is Massive

For very large tables (e.g., billions of rows), Import is impractical. DirectQuery avoids memory constraints by querying only needed data.

🔵 Source System Can Handle Query Load

DirectQuery performance is heavily dependent on the source system’s ability to process queries quickly.

Example Scenario

A company needs a report showing up-to-the-second inventory levels from an operational database.

In this case, DirectQuery is a better fit.

Trade-Offs: What You Lose With DirectQuery

While DirectQuery offers real-time access and handles large data without importing, it has limitations:

Limitation	DirectQuery Impact
Modeling flexibility	Reduced (no calculated tables; limited DAX)
Performance	Depends on underlying source and network
Query load	Heavy impact on source system if not optimized
Transformation options	Limited Power Query support

Because of these trade-offs, exam scenarios often ask you to weigh performance vs. flexibility vs. freshness.

Composite Models: Best of Both Worlds

Power BI supports composite models, which allow mixing Import and DirectQuery within the same dataset.

Use cases:

Import smaller, static reference tables
DirectQuery larger, frequently updated fact tables

Composite models provide:

Query optimization
Flexibility across mixed scenarios

Exam questions may include composite model scenarios, so understanding both modes is helpful.

Decision Criteria (Exam-Ready Framework)

When a question asks “Should you use DirectQuery or Import?”, use this decision checklist:

How current must the data be?
- Real-time → DirectQuery
- Static or refreshed periodically → Import
Can the source handle query load?
- Yes → DirectQuery
- No → Import
Is high performance critical?
- Yes and data is manageable → Import
Does modeling complexity matter?
- Yes → Import
- Minimal modeling needed → DirectQuery
Is dataset very large?
- Yes → DirectQuery
- No → Import

Typical Exam Scenarios

Here are some real-feel patterns that may appear in exam questions:

📌 Scenario: Fast Interactive Reporting

Data refreshes once per day, users need fast slicing.
Answer: Import.

📌 Scenario: Very Large Operational Tables

Real-time insight into an operational system required.
Answer: DirectQuery.

📌 Scenario: Need Most Recent Data Without Refresh Schedule

Source changes constantly; data must reflect current state.
Answer: DirectQuery.

📌 Scenario: Mixed Requirements

A small lookup table and a large facts table.
Answer: Composite model (Import + DirectQuery).

Pitfalls and How to Avoid Them

❌ Mistake: Choosing DirectQuery “just in case”

DirectQuery reduces modeling capability and can slow performance if the source isn’t optimized.

Tip: Choose DirectQuery for specific reasons—not by default.

❌ Mistake: Ignoring refresh schedule

Import requires scheduled refreshes. Forgetting this can lead to stale visuals.

Tip: Always confirm refresh requirements before choosing Import.

Key Exam Takeaways

Import = data loaded into memory (fast & flexible)
DirectQuery = data queried live (real-time & scalable)
Composite models combine both
Use decision criteria based on performance, freshness, modeling, and source capability
Always consider source capacity and query load

Understanding why you choose one mode over the other is more important than memorizing UI steps—especially on the PL-300 exam.

Practice Questions

Go to the Practice Exam Questions for this topic.

Analytics, Business Intelligence, Microsoft Certification, PL-300, Power BI January 17, 2026

Identify and Connect to Data Sources or a Shared Semantic Model (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Prepare the data (25–30%) 
    --> Get or connect to data 
        --> Identify and connect to data sources or a shared semantic model

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.

One of the first and most foundational skills tested in the PL-300: Microsoft Power BI Data Analyst exam is the ability to identify, select, and connect to appropriate data sources. This skill lives within the Prepare the data domain and underpins everything that follows—modeling, visualization, and analysis.

A Power BI Data Analyst must understand where data lives, how to connect to it efficiently, and when to reuse existing models instead of building new ones.

Understanding Data Source Types in Power BI

Power BI supports a wide variety of data sources, and the exam expects familiarity with common enterprise and self-service scenarios rather than obscure connectors.

Common Data Source Categories

File-based sources

Excel workbooks
CSV and text files
XML and JSON files
PDF files (structured tables)

Database sources

SQL Server
Azure SQL Database
Azure Synapse Analytics
Oracle
MySQL / PostgreSQL

Cloud and SaaS sources

SharePoint Online lists and files
OneDrive
Dataverse
Azure Blob Storage
Azure Data Lake Storage Gen2

Power Platform and Power BI sources

Power BI datasets (shared semantic models)
Dataflows
Datamarts

Exam tip: You are not expected to memorize every connector—focus on recognizing the correct source for a given scenario.

Choosing the Right Connectivity Mode

When connecting to data, Power BI offers three primary connectivity modes, and the exam frequently tests your understanding of when to use each one.

Import Mode

Data is loaded into the Power BI model
Fast performance
Supports full DAX and modeling capabilities
Requires dataset refreshes

Use when:
Data size is manageable and performance is critical.

DirectQuery Mode

Queries data directly at the source
Minimal data stored in Power BI
Performance depends on the source system
Some modeling and DAX limitations

Use when:
Data is very large or must remain in the source system.

Live Connection

Used with shared semantic models (Power BI datasets) or Analysis Services
No local model created
Modeling is done in the source dataset

Use when:
Connecting to a centrally governed dataset managed by another team.

Connecting to a Shared Semantic Model

A shared semantic model (formerly called a “dataset”) allows analysts to reuse existing data models instead of duplicating logic.

What Is a Shared Semantic Model?

A centrally managed Power BI dataset
Contains curated tables, relationships, measures, and security
Published to the Power BI Service
Designed for reuse across multiple reports

Benefits of Using Shared Semantic Models

Consistent metrics and definitions
Reduced duplication of logic
Centralized governance and security
Faster report development

How to Connect

In Power BI Desktop:

Select Get data
Choose Power BI datasets
Select an existing dataset from the workspace
Create a report using a Live connection

Exam tip:
When connected to a shared semantic model:

You cannot modify relationships
You cannot add calculated tables
You can create report-level measures (thin reports)

Authentication and Access Considerations

The exam also expects awareness of authentication methods, especially in enterprise environments.

Common authentication types include:

Windows authentication
Database authentication
Microsoft Entra ID (Azure AD)
OAuth

Key concept:
Your ability to connect depends on permissions at the source, not just in Power BI.

Identifying the Best Data Source for a Scenario

You may see exam questions that describe a business requirement and ask you to identify the most appropriate data source or connection method.

Example Scenarios

A finance team needs fast, interactive reports → Import mode
A large transactional database updated every minute → DirectQuery
Multiple teams need consistent KPIs → Shared semantic model
Files stored in SharePoint and updated weekly → SharePoint Folder connector

Key Exam Takeaways

For the PL-300 exam, remember the following:

Understand major data source categories
Know Import vs DirectQuery vs Live connection
Recognize when to use shared semantic models
Be aware of limitations when using Live connections
Choose data sources based on performance, governance, and scale

This topic is less about clicking buttons and more about making the right architectural decision—a critical skill for a Power BI Data Analyst.

Power BI Connection Modes – Comparison Table (PL-300 Exam Focus)

Feature / Aspect	Import	DirectQuery	Live Connection
Where data is stored	Loaded into Power BI dataset	Remains in source system	Remains in source dataset or Analysis Services
Performance	Fastest (in-memory)	Depends on source performance	Depends on source dataset
Data refresh required	Yes (scheduled or manual)	No (queries run live)	No (always live)
Data volume support	Limited by dataset size	Very large datasets	Very large datasets
Modeling allowed	Full modeling supported	Limited modeling	No model changes allowed
Create relationships	Yes	Limited	No
Create calculated tables	Yes	Limited	No
Create calculated columns	Yes	Limited	No
Create measures (DAX)	Yes	Yes (with limitations)	Yes (report-level only)
Power Query transformations	Fully supported	Limited (query folding dependent)	Not supported
Row-level security (RLS)	Supported	Supported	Inherited from source
Typical sources	Excel, CSV, SQL, SharePoint	Large SQL, Azure SQL, Synapse	Power BI datasets, Analysis Services
Best use case	High performance, small-to-medium data	Near-real-time or massive data	Reusing governed enterprise models
Exam frequency	Very high	High	Very high

When to Choose Each Mode (Exam Scenarios)

Import Mode

Choose Import when:

Performance is the top priority
Data size is manageable
Full modeling flexibility is needed

📌 Most common and safest answer on the exam unless constraints are stated.

DirectQuery

Choose DirectQuery when:

Data is extremely large
Data must stay at the source
Near real-time reporting is required

📌 Expect questions about performance trade-offs and modeling limitations.

Live Connection

Choose Live Connection when:

Using a shared semantic model
Consistent metrics are required across reports
Centralized governance is in place

📌 Often paired with “thin report” exam scenarios.

High-Value Exam Tips ⭐

If the question mentions enterprise governance or shared datasets → think Live Connection
If it mentions large, frequently changing data → think DirectQuery
If it mentions fast visuals and full modeling → think Import
Live connections do not allow model changes
Import mode requires refresh
DirectQuery relies on query folding

Practice Exam Questions

Go to the Practice Exam Questions for this section.

Analytics, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Identify and connect to data sources or a shared semantic model (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Prepare the data (25–30%) 
    --> Get or connect to data 
        --> Identify and connect to data sources or a shared semantic model

Below are 10 practice questions (with answers and explanations) for this topic of the exam. 
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions

Question 1

A report requires the fastest possible query performance and the ability to create calculated tables and columns. The dataset size is moderate and updates occur nightly. Which connection mode should you use?

A. DirectQuery
B. Live connection
C. Import
D. Composite model

✅ Correct Answer: C

Explanation:
Import mode stores data in memory, provides the best performance, and supports full modeling capabilities.

Question 2

A company has a centrally governed Power BI dataset with standardized KPIs used across multiple departments. You need to build a report using this dataset without modifying the model. What should you use?

A. Import mode
B. DirectQuery mode
C. Live connection
D. Power BI dataflow

✅ Correct Answer: C

Explanation:
Live connections are used to connect to shared semantic models and enforce centralized governance.

Question 3

Which limitation exists when using a Live connection to a Power BI dataset?

A. Measures cannot be created
B. Relationships cannot be modified
C. Data refresh cannot be scheduled
D. Visual interactions are disabled

✅ Correct Answer: B

Explanation:
When using a Live connection, the data model is read-only and cannot be changed.

Question 4

A dataset must query a transactional database containing billions of rows with near real-time requirements. The data must remain at the source system. Which connection mode is most appropriate?

A. Import
B. Live connection
C. DirectQuery
D. Power BI dataflow

✅ Correct Answer: C

Explanation:
DirectQuery queries the data directly at the source and supports very large datasets.

Question 5

You connect to a shared semantic model in Power BI Service. Which type of measures can you create?

A. Calculated column measures
B. Dataset-level measures
C. Report-level measures only
D. No measures can be created

✅ Correct Answer: C

Explanation:
With a Live connection, only report-level measures are allowed.

Question 6

Which data source scenario is best suited for Import mode?

A. Streaming IoT telemetry
B. Large fact table updated every second
C. Excel files stored in SharePoint and refreshed weekly
D. Enterprise semantic model shared across teams

✅ Correct Answer: C

Explanation:
File-based sources with scheduled refreshes are ideal for Import mode.

Question 7

Which statement about DirectQuery is true?

A. All Power Query transformations are supported
B. Data is cached in memory for performance
C. Query performance depends on the source system
D. Calculated tables are fully supported

✅ Correct Answer: C

Explanation:
DirectQuery sends queries to the source system, so performance depends on the source.

Question 8

When should you connect to a Power BI dataset instead of importing data?

A. When you need to modify relationships
B. When consistent metrics across reports are required
C. When data volume is small
D. When Power Query transformations are required

✅ Correct Answer: B

Explanation:
Shared semantic models ensure consistency and governance across reports.

Question 9

Which authentication method is commonly used when connecting to cloud-based Power BI datasets?

A. Database authentication
B. Windows authentication
C. OAuth / Microsoft Entra ID
D. Anonymous authentication

✅ Correct Answer: C

Explanation:
Power BI Service connections typically use Microsoft Entra ID (OAuth).

Question 10

A Power BI report must use data from both an imported Excel file and a DirectQuery SQL source. Which feature enables this scenario?

A. Live connection
B. Power BI dataflow
C. Composite model
D. Shared semantic model

✅ Correct Answer: C

Explanation:
Composite models allow combining Import and DirectQuery data sources.

Exam Readiness Check ✅

If you can:

Quickly identify Import vs DirectQuery vs Live
Recognize shared semantic model scenarios
Recall modeling limitations by connection type

…then you’re in great shape for this PL-300 objective.

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

Analytics, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Change data source settings, including credentials and privacy levels (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
  --> Get or connect to data
    --> Change data source settings, including credentials and privacy levels

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions

Question 1

A dataset refresh succeeds in Power BI Desktop but fails after being published to the Power BI Service. What is the most likely cause?

A. Privacy levels were set incorrectly in Desktop
B. Credentials were not configured in the Power BI Service
C. Query folding was disabled
D. The dataset uses Import mode

✅ Correct Answer: B

Explanation:
Credentials used in Power BI Desktop do not automatically transfer to the Power BI Service. They must be configured separately.

Question 2

Which authentication method is most commonly used when connecting to cloud-based data sources in the Power BI Service?

A. Windows authentication
B. Database authentication
C. OAuth (Microsoft Entra ID)
D. Anonymous authentication

✅ Correct Answer: C

Explanation:
Cloud data sources typically use OAuth through Microsoft Entra ID for secure authentication.

Question 3

What is the primary purpose of privacy levels in Power BI?

A. To encrypt data at rest
B. To restrict report sharing
C. To prevent unintended data leakage when combining sources
D. To improve dataset refresh performance

✅ Correct Answer: C

Explanation:
Privacy levels isolate data sources to prevent sensitive data from being unintentionally shared across sources.

Question 4

A Power BI model combines data from a Private SQL database and a Public web source. What is a potential impact of this configuration?

A. Scheduled refresh will be disabled
B. Query folding may be prevented
C. Calculated measures will not work
D. Row-level security will be removed

✅ Correct Answer: B

Explanation:
When combining sources with different privacy levels, Power BI may prevent query folding to enforce data isolation.

Question 5

Which privacy level should be assigned to internal corporate data sources?

A. Public
B. Anonymous
C. Organizational
D. Private

✅ Correct Answer: C

Explanation:
Organizational privacy level is intended for internal company data that can be shared within the organization.

Question 6

Which statement about the “Ignore privacy levels” option is true?

A. It applies to both Desktop and Service
B. It permanently disables privacy enforcement
C. It only applies in Power BI Desktop
D. It improves dataset refresh performance in the Service

✅ Correct Answer: C

Explanation:
Ignoring privacy levels is a Desktop-only setting and is not honored during refresh in the Power BI Service.

Question 7

When are credentials typically required to be re-entered?

A. When a report is viewed by another user
B. When the dataset owner changes
C. When a visual is modified
D. When a slicer is added

✅ Correct Answer: B

Explanation:
Changing dataset ownership often requires credentials to be re-entered to maintain refresh functionality.

Question 8

Which scenario requires the use of an on-premises data gateway?

A. Connecting to Azure SQL Database
B. Connecting to a Power BI dataset
C. Connecting to an on-premises SQL Server from the Service
D. Connecting to SharePoint Online

✅ Correct Answer: C

Explanation:
On-premises data sources require a gateway to allow the Power BI Service to access them.

Question 9

What happens if incorrect credentials are configured in the Power BI Service?

A. The report will not open
B. Visuals will display blank data
C. Scheduled refresh will fail
D. The dataset will be deleted

✅ Correct Answer: C

Explanation:
Incorrect credentials cause scheduled refresh failures, even if reports still open using cached data.

Question 10

Which action should you take first when a scheduled refresh suddenly starts failing?

A. Rebuild the data model
B. Change privacy levels
C. Check and update credentials in the Service
D. Disable query folding

✅ Correct Answer: C

Explanation:
Most unexpected refresh failures are caused by expired or changed credentials and should be checked first.

Exam Readiness Summary ✅

If you can confidently:

Distinguish Desktop vs Service credentials
Explain why privacy levels exist
Identify refresh and gateway issues
Understand how privacy levels affect query folding

…then you are well prepared for this PL-300 exam objective.

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

Analytics, Business Intelligence, Data Education & Training, Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Choose between DirectQuery and Import (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Get or connect to data
      --> Choose between DirectQuery and Import

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions

Question 1

A Power BI report must deliver the fastest possible visual response. The dataset is moderate in size and refreshed once per day. Which connectivity mode should you choose?

A. DirectQuery
B. Live connection
C. Import
D. Composite model

✅ Correct Answer: C

Explanation:
Import mode stores data in memory, providing the fastest performance and full modeling capabilities.

Question 2

A report must show up-to-the-minute transaction data from a large operational database. Data must remain in the source system. What is the best option?

A. Import
B. DirectQuery
C. Live connection
D. Power BI dataflow

✅ Correct Answer: B

Explanation:
DirectQuery retrieves data directly from the source in real time and avoids importing large datasets.

Question 3

Which limitation is most commonly associated with DirectQuery?

A. No scheduled refresh support
B. Reduced modeling and DAX capabilities
C. Inability to use row-level security
D. Inability to connect to SQL Server

✅ Correct Answer: B

Explanation:
DirectQuery limits certain modeling features, including calculated tables and some DAX expressions.

Question 4

A dataset contains a small product lookup table and a very large fact table that updates continuously. What is the most appropriate solution?

A. Import both tables
B. Use DirectQuery for both tables
C. Use a composite model
D. Use a live connection

✅ Correct Answer: C

Explanation:
Composite models allow importing small static tables while using DirectQuery for large, frequently updated tables.

Question 5

Which factor has the greatest impact on report performance when using DirectQuery?

A. Number of visuals on the page
B. Power BI Desktop version
C. Performance of the source system
D. Dataset refresh frequency

✅ Correct Answer: C

Explanation:
DirectQuery sends queries to the source system, so performance depends heavily on the source’s ability to handle queries.

Question 6

When is Import mode generally not recommended?

A. When modeling flexibility is required
B. When dataset size exceeds practical memory limits
C. When reports need fast interactivity
D. When refresh can occur on a schedule

✅ Correct Answer: B

Explanation:
Very large datasets may exceed memory constraints, making Import impractical.

Question 7

Which statement about data freshness is true?

A. Import mode always shows real-time data
B. DirectQuery requires scheduled refresh
C. Import mode relies on dataset refresh
D. DirectQuery stores data in memory

✅ Correct Answer: C

Explanation:
Import mode displays data as of the last refresh, while DirectQuery retrieves data at query time.

Question 8

A Power BI report must support complex DAX measures and calculated tables. Data updates hourly and does not need real-time accuracy. What should you choose?

A. DirectQuery
B. Import
C. Live connection
D. Streaming dataset

✅ Correct Answer: B

Explanation:
Import mode supports full DAX and modeling flexibility and is appropriate when real-time data is not required.

Question 9

Which scenario is the best candidate for DirectQuery?

A. Monthly financial reporting
B. Historical trend analysis
C. Real-time inventory monitoring
D. Static reference data

✅ Correct Answer: C

Explanation:
Real-time or near-real-time monitoring scenarios are ideal for DirectQuery.

Question 10

Why might a Power BI Data Analyst avoid DirectQuery unless necessary?

A. It cannot connect to cloud data sources
B. It disables report sharing
C. It can negatively impact performance and modeling flexibility
D. It does not support security

✅ Correct Answer: C

Explanation:
DirectQuery introduces performance dependencies on the source system and limits modeling features, making Import preferable when possible.

Exam Readiness Check ✅

You’re well prepared for this PL-300 objective if you can:

Identify real-time vs scheduled refresh needs
Balance performance vs flexibility
Recognize large-scale data scenarios
Explain why DirectQuery is chosen—not just when

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

Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Create and modify parameters (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Get or connect to data
      --> Create and modify parameters

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions

Question 1

What is the primary purpose of using parameters in Power Query?

A. To improve visual rendering performance
B. To store calculated DAX expressions
C. To replace hard-coded values with reusable variables
D. To control report-level security

✅ Correct Answer: C

Explanation:
Parameters allow you to define reusable values that replace hard-coded inputs such as server names, file paths, or filter values.

Question 2

Which scenario is the best use case for Power Query parameters?

A. Creating dynamic slicers for end users
B. Switching between development and production databases
C. Defining relationships between tables
D. Creating calculated measures

✅ Correct Answer: B

Explanation:
Parameters are commonly used to manage environment-specific values like server and database names.

Question 3

Where are parameters created and managed in Power BI Desktop?

A. Report view
B. Model view
C. DAX editor
D. Power Query Editor

✅ Correct Answer: D

Explanation:
Parameters are created and modified in the Power Query Editor under Manage Parameters.

Question 4

Which data types can be assigned to a Power Query parameter?

A. Text only
B. Numeric only
C. Any DAX-supported data type
D. Text, Number, Date, DateTime, and Boolean

✅ Correct Answer: D

Explanation:
Power Query parameters support multiple data types, including text, numeric, and date-based types.

Question 5

What happens when you change the current value of a parameter that is used in a query?

A. The report visuals automatically change without refresh
B. Only the parameter value updates, not the data
C. The dependent queries are refreshed using the new value
D. The parameter is deleted and recreated

✅ Correct Answer: C

Explanation:
Changing a parameter’s current value causes any dependent queries to refresh using the updated value.

Question 6

Which option helps restrict users to predefined parameter values?

A. Required
B. Suggested values
C. Parameter type
D. Query folding

✅ Correct Answer: B

Explanation:
Suggested values allow you to define a list or range of acceptable parameter inputs.

Question 7

A Power BI dataset uses a parameter to define a file path. The file location changes. What is the most efficient way to update the dataset?

A. Rewrite the M code
B. Replace the query entirely
C. Update the parameter value
D. Create a calculated column

✅ Correct Answer: C

Explanation:
Updating the parameter value avoids editing query logic and ensures all dependent queries update automatically.

Question 8

Which statement about Power Query parameters is true?

A. Parameters can only be used for filtering rows
B. Parameters must be created using M code
C. Parameters appear as queries in Power Query
D. Parameters are only supported in Import mode

✅ Correct Answer: C

Explanation:
Parameters appear as special queries in Power Query and can be referenced by other queries.

Question 9

A parameter is used to filter data in a query. What happens if the parameter’s value does not match the expected data type?

A. Power BI automatically converts the value
B. The report loads with blank visuals
C. The query refresh may fail
D. The parameter is ignored

✅ Correct Answer: C

Explanation:
A mismatch between parameter type and usage can cause query errors during refresh.

Question 10

How are parameters most commonly tested on the PL-300 exam?

A. Memorizing the M syntax used to create them
B. Identifying UI navigation steps
C. Selecting the correct scenario where parameters improve flexibility
D. Writing custom functions

✅ Correct Answer: C

Explanation:
The exam focuses on when and why to use parameters rather than detailed syntax or UI steps.

Exam Readiness Summary ✅

You are well prepared for this topic if you can:

Explain why parameters are used
Identify environment and filtering scenarios
Understand how parameter changes affect refresh
Recognize parameters as a Power Query feature, not DAX

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

Microsoft Certification, PL-300, Power BI January 17, 2026

Practice Questions: Evaluate Data including Data Statistics & Column Properties (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Profile and clean the data
      --> Evaluate Data including Data Statistics & Column Properties

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions

Question 1

Which Power Query feature helps you quickly identify null, error, and valid values in a column?

A. Column distribution
B. Column profile
C. Column quality
D. Data type detection

✅ Correct Answer: C

Explanation:
Column quality visually shows the percentage of valid, error, and empty (null) values in a column.

Question 2

You want to understand how often each value appears in a categorical column. Which tool should you use?

A. Column quality
B. Column distribution
C. Column profile
D. Default summarization

✅ Correct Answer: B

Explanation:
Column distribution displays value frequency and helps identify duplicates or unexpected values.

Question 3

Which Power Query tool provides statistics such as minimum, maximum, and average values?

A. Column quality
B. Column distribution
C. Column profile
D. Data view

✅ Correct Answer: C

Explanation:
Column profile provides detailed statistical information for numeric and date columns.

Question 4

A numeric column cannot be aggregated in a visual because it is treated as text. What should you evaluate first?

A. Column distribution
B. Column format
C. Column data type
D. Default summarization

✅ Correct Answer: C

Explanation:
Incorrect data types prevent aggregation. The data type should be corrected before formatting or summarization.

Question 5

Which setting determines how a numeric column behaves by default when added to a visual?

A. Column profile
B. Data type
C. Column format
D. Default summarization

✅ Correct Answer: D

Explanation:
Default summarization controls whether a numeric column sums, averages, counts, or does not summarize.

Question 6

When should data evaluation and profiling primarily take place?

A. In Report view
B. In Model view
C. In Power Query Editor
D. In the Power BI Service

✅ Correct Answer: C

Explanation:
Data profiling and evaluation are performed in Power Query before data is loaded into the model.

Question 7

You suspect a column contains unexpected negative values. Which feature helps confirm this?

A. Column quality
B. Column distribution
C. Column profile
D. Column format

✅ Correct Answer: C

Explanation:
Column profile provides min/max statistics, making it ideal for detecting invalid ranges.

Question 8

Which column property affects how values are displayed but not how they are calculated?

A. Data type
B. Column format
C. Column profile
D. Default summarization

✅ Correct Answer: B

Explanation:
Formatting controls visual appearance (currency, percentage, date format) but does not affect calculations.

Question 9

A column shows a high percentage of error values during evaluation. What is the most likely implication?

A. The column has duplicate values
B. The column contains invalid or incompatible data
C. The column is incorrectly summarized
D. The column has incorrect formatting

✅ Correct Answer: B

Explanation:
Error values typically indicate issues such as invalid conversions or incompatible data types.

Question 10

Which pairing of Power Query tools and purpose is correct?

A. Column quality → value frequency analysis
B. Column distribution → identifying null values
C. Column profile → statistical analysis
D. Column format → detecting outliers

✅ Correct Answer: C

Explanation:
Column profile is used for statistical analysis, including min, max, and average values.