PL-300 Exam Study Guide – Free Practice Questions

PL-300 Microsoft Power BI Data Analyst Study Guide

How to Prepare for the PL-300 Exam?

Preparing for the PL-300 Microsoft Power BI Data Analyst Certificate exam? Don’t know where to start? This post is the PL-300 Certificate Study Guide (with links to each exam objective).

I have curated a list of articles from Microsoft documentation for each objective of the PL-300 exam. I hope this article will help you to achieve the Microsoft Certified Data Analyst Associate Certificate. Also, please share the post within your circles so it helps them to prepare for the exam.

Exam Voucher for PL-300 with 1 Retake

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Practice Tests for PL-300 Microsoft Power BI

Udemy Practice TestMS Power BI Analyst Practice Test (with code)
Amazon e-book (PDF)Analyze data with Power BI

PL-300 Analyzing Data with Microsoft Power BI

UdemyAnalyzing Data with Microsoft Power BI
Pluralsight (Learning Path)Microsoft Power BI for Analysts [Free Trial]

Microsoft Power BI Projects

Udacity (Nanodegree)Data Analysis & Visualization with Power BI
Coursera Guided ProjectSales Analysis using Power BI
Datacamp (Interactive)Introduction to Microsoft Power BI

PL-300 Sample Practice Exam Questions

PL-300 certification sample practice EXAM questions (MICROSOFT POWER BI Data Analyst)

PL-300 vs. DA-100. What’s changed?

Most of the content topics are the same between PL-300 and DA-100. However, here are some differences:

Removed the section Profile the data (entire submodule)

Added AI visuals

Removed major part of the sections on Enhance reports to expose insights and Perform advanced analysis.

For more details check out my PL-300 vs. DA-100 video.

Check out all the other Power Platform certificate study guides

Prepare the Data (15-20%)

Get Data from Different Data Sources

Identify and connect to a data source

Data sources in Power BI desktop

Connect to data in Power BI desktop

Change data source settings

Manage data sources

Select a shared dataset or create a local dataset

Where your workbook file is saved makes a difference

Power BI shared datasets

Select a storage mode

Storing data in the Power BI file

Use Microsoft Dataverse

What is Microsoft Dataverse?

Get started with Dataverse

Change the value in a parameters

Specify parameters for the data source in Power BI

Edit parameter settings in the Power BI service

Connect to a data flow

Configure and consume a dataflow

Creating a dataflow

Clean, Transform, and Load the Data

Profile the data

Using the data profiling tools

Column Profiling in Power BI Desktop

Resolve inconsistencies, unexpected or null values, and data quality issues

Inconsistencies with date-type fields

Tips & tricks for creating relationships in Power BI Desktop

Relationships in Power BI Desktop when the data has null or blank values

How to Spot and Improve Data Quality in Power BI

Identify and create appropriate keys for joins

Model relationships in Power BI Desktop

Evaluate and transform column data types

Evaluate & change column data types

Shape and transform tables

Shape & combine data in Power BI Desktop

Combine queries

Merging queries

Apply user-friendly naming conventions to columns and queries

Data import best practices in Power BI

Configure data loading

New updates in Power Query

Resolve data import errors

Resolve data import errors

PL-300 Introducing Microsoft Power BI

Amazon link (affiliate)

Model the Data (30-35%)

Design a Data Model

Define the tables

Tables in Power BI reports and dashboards

Configure table and column properties

Format the table

Adjust the column width of a table

Design and implement role-playing dimensions

Role-playing dimensions

Define a relationship’s cardinality and cross-filter direction

The Cardinality option

Cross filter direction

Design a data model that uses a star schema

Understand star schema and the importance for Power BI

Power BI: Star schema or single table

Create a common date table

Common Date Filter for Multiple Tables

Develop a Data Model

Create calculated tables

Create calculated tables in Power BI Desktop

Test your knowledge of Calculated tables

Q] This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

An employee works with a data model with tables and relationships defined below.

He creates a 2-column calculated table [Product subcategory, Average Sales] that returns only the product subcategories whose average sales are higher than the average sales of all the products. He has to complete the below DAX expression:

Solution: He chooses [Average Sales] and [Average Sales] in the 1st and 2nd boxes, respectively.

Does the solution solve the problem?

  1. Yes
  2. No


Recall the golden rule on context transition from Practice Test 1:

If you use the CALCULATE function in a row context, context transition is automatic.

Since ADDCOLUMNS evaluates the expression in a row context, using an explicit or implicit (introduced by a measure) CALCULATE function will force context transition to happen. i.e., row values of Product subcategory in the virtual table filter columns of the Sales table.

  1. Using a measure (where CALCULATE is implicit) in a row context will force context transition.
  2. The row value (Touring Bikes) filters the Product table for the relevant Product subcategories.
  3. The filtered Product table filters the Sales table for the related products.

So, [Average Sales] will calculate only the average sales of the specific product subcategories. [Average Sales] does go into the 1st box.

To achieve the desired result, we only need to return product subcategories whose average sales are higher than the average sales of all products.

Since the outer FILTER function too is an iterator, it evaluates the filter expression in row context. So, using the measure [Average Sales] in the 2nd box will force context transition (for the same reason above) and return the average sales of the currently iterating product subcategory.

In the filter expression, the average sales of any subcategory are compared with itself. Since the operator in the expression is >=, we get all the product subcategories. There is no filter happening here.

The blank rows are because the products in these product subcategories do not have any records in the Sales table.

So, selecting [Average Sales] in the 2nd box is incorrect. The given set of choices doesn’t completely solve the problem. Option No is the correct answer.

Reference Link:

Note: When beginning with DAX, similar to SQL, it is helpful to think as one table filters the other via relationships. But, as you learn more about DAX, you should be able to think in terms of expanded tables (and not relationships).

Reference Link:

Knowledge Area: Model the data

This question is part of my course PL-300 Exam Questions. It is copyrighted and cannot be reproduced elsewhere without permission.

Create hierarchies

Create a hierarchy in Power BI

Create calculated columns

Create calculated columns in the Power BI desktop

Implement row-level security roles

Restrict data access with RLS for Power BI Desktop

Use the Q&A feature

Use Power BI Q&A to explore your data & create visuals

Q&A for Power BI business users

Create Model Calculations by Using DAX

Create basic measures by using DAX

Create & use your own measures

DAX: Use variables to improve your formulas

Use CALCULATE to manipulate filters

DAX: Avoid using FILTER as a filter argument

Implement Time Intelligence using DAX

Time intelligence in Power BI desktop

Replace implicit measures with explicit measures

Explicit vs, implicit DAX measures in Power BI

Use basic statistical functions

DAX Statistical Functions

Create semi-additive measures

Semi additive measures in DAX for Power pivot

Use quick measures

Use quick measures for common calculations

Test your knowledge of Quick Measures

Q] You analyze sales data of a sports equipment company. You observe a lot of fluctuations in the data, such as low sales mid-week and high sales on Friday and Saturday.

Your manager asked you to create a quick measure for the weekly rolling average to smooth out daily fluctuations.

How would you complete the quick measure?

  1. Days, 7, 7
  2. Days, 6, 6
  3. Days, 6, 0
  4. Days, 7, 0
  5. Days 7, 1


For calculating the weekly rolling average, the Period has to be Days.

Rolling averages include data until the current period (in this case, the day) for calculation.

Reference Link:

It makes sense, as in a weekly rolling average calculation, you do not want a blank value for the first date in the table. For January 1, Total Sales and the Total Sales rolling average is 55. If the rolling average calculation does not include the current period, January 1 would have blank values.

Since rolling averages already include the current period, for a weekly (7-day) rolling average, the Periods before should be 6. And, Periods after should be 0 since we do not want to include future dates into the calculation.

Option Days, 6, 0 is the correct answer.

Reference Link:

Knowledge Area: Model the data

This question is part of my course PL-300 Exam Questions. It is copyrighted and cannot be reproduced elsewhere without permission.

Optimize Model Performance

Remove unnecessary rows and columns

Delete records or rows if the blank field

Remove Columns from Tables in Power BI

Identify poorly performing measures, relationships, and visuals

Slow Measures

Dealing with slow measures

Slow Power BI report

Table relationship causes severe performance drop

Power BI Desktop Visuals slow load

Reduce cardinality levels to improve performance

Reduce cardinality

Optimize high cardinality columns in VertiPaq

Visualize and Analyze the Data (25-30%)

Create Reports

Add visualization items to reports

Add visuals to a Power BI report

Choose an appropriate visualization type

Visualization types in Power BI

Format and configure visualizations

Getting started with the formatting pane

Use a custom visual

Power BI visual files

Apply and customize a theme

Use report themes in Power BI Desktop

Use dashboard themes in the Power BI service

Configure conditional formatting

Use conditional formatting in tables

Apply slicing and filtering

Slicers in Power BI

Add a filter to a report in Power BI

Configure the report page

Change the display of a report page

Use the Analyze in Excel feature

Start in Power BI with Analyze in Excel

Choose when to use a paginated report

When to use paginated reports in Power BI?

Create Dashboards

Manage tiles on a dashboard

Edit or remove a dashboard tile

Configure mobile view

Optimize a dashboard for mobile phones

Use the Q&A feature

Q&A on dashboards

Add a Quick Insights result to a dashboard

Run and view insights on dashboard tiles

Apply a dashboard theme

Use dashboard themes in the Power BI service

Pin a live report page to a dashboard

Add a new dashboard tile is by pinning an entire report page

Enhance Reports for Usability and Storytelling

Configure bookmarks

Bookmarks in Power BI desktop to share insights & build stories

Create custom tooltips

Customize tooltips in Power BI desktop

Edit and configure interactions between visuals

Change how visuals interact in a Power BI report

Configure navigation for a report

Make navigation easier with Power BI buttons

Apply sorting

Sort by column in Power BI Desktop

Configure Sync Slicers

Sync & use slicers on other pages

Group and layer visuals by using the selection pane

Power BI: Explore the new selection pane feature

Drilldown into data using interactive visuals

Drill mode in a visual in Power BI

Export report data

Export data from a visual in a report

Design reports for mobile devices

Optimize Power BI reports for the mobile app

Power BI Design for Mobile Device

Use the Analyze feature in Power BI

Use the Analyze feature to explain fluctuations in report visuals

Identify outliers

Identify outliers with Power BI visuals

How to detect anomalies & outliers In your data

Choose between continuous and categorical axes

Customize X-axis and Y-axis properties

Use groupings, binnings, and clustering

Use grouping and binning in Power BI desktop

Implement clustering in Power BI

Use AI visuals

Work with AI visuals in Power BI

Use the Forecast feature

Power BI forecasting

Forecasting in Power BI

Create reference lines by using the Analytics pane

Use the Analytics pane

Deploy and Maintain Assets (20-25%)

Manage Files and Datasets

Identify when a gateway is required

Solved: Do I need a gateway?

Power BI Gateway – A complete guide covering all the major aspects

Configure a dataset scheduled refresh

Configure scheduled refresh

Configure row-level security group membership

Power BI Row-level Security Groups

Row-level security using AD groups

Providing access to datasets

Build permission for shared datasets

Manage global options for files

Change settings for Power BI report

Manage Workspaces

Create and configure a workspace

Create the new workspaces in Power BI

Manage your workspace in Power BI & Microsoft 365

Assign workspace roles

Roles in the new workspaces

Configure and update a workspace app

Change your published app

Publish, import, or update assets in a workspace

Publish an app in Power BI

Apply sensitivity labels to workspace content

How to apply sensitivity labels in Power BI?

Configure subscriptions and data alerts

Subscribe to a report or dashboard

Subscribe yourself to reports & dashboards

Set data alerts in the Power BI service

Promote or certify Power BI content

Promote your reports, dashboards, & apps

Endorse your content

Endorsement – Promote & certify Power BI content

This brings us to the end of the PL-300 Microsoft Power BI Data Analyst Study Guide.

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are preparing for other Power Platform certification exams, check out the Power Platform study guides for those exams.

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