- A
Disable auto-date/time feature for the model.
Auto-date/time creates hidden date tables for each date column, increasing model size. Disabling it reduces overhead.
- B
Use integer surrogate keys instead of string keys for dimensions.
Integer keys are more efficient for storage and joins than string keys.
- C
Combine the fact table with dimension tables into a single wide table.
Why wrong: Denormalizing into a single wide table increases row size and can hurt performance; star schema is preferred.
- D
Create calculated columns in the fact table instead of in Power Query.
Why wrong: Calculated columns are stored in memory and increase model size; better to create them in Power Query or as measures.
- E
Remove unnecessary columns from the fact table.
Fewer columns mean less data to load and store, improving refresh and query performance.
Quick Answer
The answer is to remove unnecessary columns from the fact table, disable the auto-date/time feature, and choose the appropriate storage mode. Removing unnecessary columns reduces the model’s memory footprint and speeds up data refresh by limiting the volume of data loaded from Azure Synapse Analytics. Disabling auto-date/time prevents Power BI from generating hidden date tables for every date column, which can bloat the model and degrade query performance, especially with large fact tables. Selecting the correct storage mode—such as DirectQuery for real-time needs or Import for faster aggregations—balances memory usage against query speed. On the PL-300 exam, this scenario tests your ability to apply foundational optimization techniques to enterprise-scale models, often appearing as a multi-select question where traps include keeping auto-date/time enabled or retaining all columns for flexibility. Remember the mnemonic “RAD: Remove, Auto-off, DirectQuery” to recall these three critical actions for optimizing Power BI semantic model performance.
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are developing a Power BI semantic model that uses a large fact table from Azure Synapse Analytics. You need to optimize the model for performance. Which THREE actions should you take?
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
Disable auto-date/time feature for the model.
Option A is correct because disabling the auto-date/time feature prevents Power BI from automatically creating hidden date tables for each date column, which can significantly increase model size and processing time. This is especially important when working with large fact tables from Azure Synapse Analytics, as it reduces memory consumption and improves query performance by eliminating unnecessary overhead.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Disable auto-date/time feature for the model.
Why this is correct
Auto-date/time creates hidden date tables for each date column, increasing model size. Disabling it reduces overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use integer surrogate keys instead of string keys for dimensions.
Why this is correct
Integer keys are more efficient for storage and joins than string keys.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Combine the fact table with dimension tables into a single wide table.
Why it's wrong here
Denormalizing into a single wide table increases row size and can hurt performance; star schema is preferred.
- ✗
Create calculated columns in the fact table instead of in Power Query.
Why it's wrong here
Calculated columns are stored in memory and increase model size; better to create them in Power Query or as measures.
- ✓
Remove unnecessary columns from the fact table.
Why this is correct
Fewer columns mean less data to load and store, improving refresh and query performance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think combining tables into a wide table simplifies the model, but this actually degrades performance by breaking star schema design principles, which are critical for efficient query processing in Power BI.
Detailed technical explanation
How to think about this question
Disabling auto-date/time reduces the number of columns and tables in the model, which directly impacts the VertiPaq engine's compression and storage efficiency. In large fact tables, every additional column increases the dictionary size and memory footprint, so removing unnecessary columns (Option E) and using integer surrogate keys (Option B) further optimize compression by reducing cardinality and enabling better encoding. Under the hood, integer keys allow for more efficient relationship joins and faster aggregation, as they avoid string comparisons and reduce the size of the relationship metadata.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PL-300 question test?
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Disable auto-date/time feature for the model. — Option A is correct because disabling the auto-date/time feature prevents Power BI from automatically creating hidden date tables for each date column, which can significantly increase model size and processing time. This is especially important when working with large fact tables from Azure Synapse Analytics, as it reduces memory consumption and improves query performance by eliminating unnecessary overhead.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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