The correct answer is to ensure the fact table and dimension tables follow a star schema design with proper relationships. This design optimizes Power BI performance by organizing data into a central fact table surrounded by dimension tables, which minimizes relationship cardinality and reduces model size. The star schema enables the VertiPaq storage engine to compress data more efficiently and allows query folding, meaning filters applied across multiple dimensions are resolved faster without scanning the entire fact table. On the DP-900 exam, this concept tests your understanding of foundational data modeling best practices, often appearing as a scenario where a slow dashboard is blamed on a flat or snowflake schema. A common trap is assuming adding more indexes or aggregations alone will fix the issue, but the root cause is structural. Memory tip: think of a star—the fact table is the bright center, and dimensions are the points; keep them separate and connected, not tangled.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Your company has a Power BI dashboard that uses a data model with a single large fact table and several dimension tables. The dashboard loads slowly when users filter by multiple dimensions. Which design change would MOST improve performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ensure the fact table and dimension tables follow a star schema design with proper relationships.
Option C is correct because a star schema design with proper relationships between the fact table and dimension tables is the foundational best practice for optimizing Power BI data models. This design minimizes the cardinality of relationships, reduces the size of the data model, and enables efficient query folding and storage engine compression, which directly improves filter performance across multiple dimensions.
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.
✗
Use page-level filters instead of report-level filters.
Why it's wrong here
Filter scope does not significantly impact performance.
✗
Create a calculated table that aggregates the fact table at a higher granularity.
Why it's wrong here
This reduces detail but may not improve filter performance across dimensions.
✓
Ensure the fact table and dimension tables follow a star schema design with proper relationships.
Why this is correct
Star schema is optimized for analytical queries and filtering.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert the data model to a composite model using DirectQuery for some tables.
Why it's wrong here
Composite models can still be slow if underlying queries are inefficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse UI-level filter scoping (page-level vs. report-level) with actual query performance optimization, or they mistakenly believe that aggregating data or switching to DirectQuery will always improve speed, ignoring the fundamental importance of star schema design for in-memory analytics.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's VertiPaq storage engine heavily relies on columnar compression and relationship cardinality to reduce memory footprint and speed up filter propagation. A star schema ensures that dimension tables are small and highly compressed, allowing filter operations to be resolved via hash joins in the storage engine rather than slower loop joins or cross-filtering across large tables. In real-world scenarios, a single large fact table without proper dimension tables forces Power BI to scan the entire table for every filter, whereas a star schema with proper relationships enables the engine to push filters down to the dimension tables first, drastically reducing the number of rows scanned.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Ensure the fact table and dimension tables follow a star schema design with proper relationships. — Option C is correct because a star schema design with proper relationships between the fact table and dimension tables is the foundational best practice for optimizing Power BI data models. This design minimizes the cardinality of relationships, reduces the size of the data model, and enables efficient query folding and storage engine compression, which directly improves filter performance across multiple dimensions.
What should I do if I get this DP-900 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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