PL-300 Model the data Practice Question
You are a data analyst for a manufacturing company. You have a Power BI semantic model that imports data from an Azure SQL Database. The model contains a fact table named Production and a dimension table named Products. The Products table has columns ProductID, ProductName, Category, and Subcategory. You need to reduce the model size and improve query performance. Which two actions should you take? (Choose two.)
⚠ Common exam trap
The trap here is assuming that cosmetic settings like 'Don't summarize' or hierarchies affect performance, when they are purely metadata or usability features.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the data type of ProductID from Text to Whole Number.
Removing unused columns and converting numeric keys stored as text to whole numbers both reduce the model's memory footprint and improve query performance. Unused columns add unnecessary cardinality and storage, while text columns are less efficient than numeric types. The other options either affect only metadata or increase model size.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the data type of ProductID from Text to Whole Number.
Why this is correct
ProductID is likely a numeric identifier. Storing it as a whole number instead of text reduces storage because numeric data types compress better and require less memory. This improves performance and reduces model size, assuming the values are indeed numeric and used as a key.
- ✗
Enable 'Auto date/time' for the Production table.
Why it's wrong here
Enabling Auto date/time creates hidden date tables for each date column, which increases model size and can degrade performance. It is generally recommended to disable this feature and use a dedicated date table instead. It does not help reduce size or improve query performance.
- ✓
Remove unused columns from the Products table.
Why this is correct
Removing columns that are not used in any report, relationship, or measure reduces the model's memory footprint and can improve processing and query performance. This is a standard optimization for imported models, especially when the table contains descriptive attributes that are not needed for analysis.
- ✗
Create a hierarchy for Category and Subcategory.
Why it's wrong here
Creating a hierarchy is a usability enhancement for report authors, but it does not reduce model size or improve query performance. Hierarchies are metadata objects and do not change the underlying storage or compression of the columns involved.
- ✗
Set the Category and Subcategory columns to use the 'Summarize by: Don't summarize' property.
Why it's wrong here
This setting only affects implicit measures in client tools; it does not reduce model size or improve query performance. The columns still consume memory and are processed during refresh. While it can prevent accidental aggregation, it is not a size or performance optimization.
Go deeper
Related to this question
About these practice questions
One of 524 original PL-300 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.