The correct answer is the star schema design with dimension and fact tables. This pattern is ideal because it separates quantitative business measures, such as sales amounts or order counts, into fact tables, while storing descriptive attributes like customer names or dates in normalized dimension tables. By joining facts with a date dimension, you can easily slice data for historical trend analysis, and by filtering on the most recent date key, you support current-day reporting—all while minimizing storage redundancy through shared dimensions. On the DP-900 exam, this question tests your understanding of Azure Synapse Analytics optimization, as the platform’s columnstore indexes and hash-distributed tables are built to accelerate star schema queries. A common trap is choosing a snowflake schema, which adds extra normalization and joins that hurt performance in Synapse. Memory tip: think of a star’s center (fact) and its points (dimensions)—keep it simple for speed.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. 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.
Your team is migrating a data warehouse to Azure Synapse Analytics. You need to ensure that the data model supports both historical trend analysis and current-day reporting with minimal storage redundancy. Which table design pattern should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Star schema with dimension and fact tables
The star schema is the correct choice because it separates business processes into fact tables (for measures like sales quantities) and dimension tables (for descriptive attributes like customer or date). This design directly supports both historical trend analysis (by joining facts with the date dimension) and current-day reporting (by filtering on the latest date) while minimizing storage redundancy through normalized dimensions. Azure Synapse Analytics is optimized for star schemas, leveraging columnstore indexes and distributed tables to accelerate such queries.
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.
✗
Single flat table containing all attributes
Why it's wrong here
Flat tables cause data redundancy and slow down query performance.
✗
Wide table with repeated customer attributes per order
Why it's wrong here
Repeating attributes increases storage and maintenance overhead.
✗
Highly normalized design with many tables
Why it's wrong here
Normalization is for OLTP systems; it complicates analytical queries.
✓
Star schema with dimension and fact tables
Why this is correct
Star schema is the standard for data warehousing, enabling efficient queries and reducing storage redundancy.
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 confuse 'normalization' (Option C) with data warehouse best practices, not realizing that star schemas intentionally denormalize dimensions to optimize for read-heavy analytical queries, while highly normalized designs are better suited for OLTP systems, not Azure Synapse Analytics.
Detailed technical explanation
How to think about this question
In Azure Synapse, star schema fact tables are typically hash-distributed on a foreign key (e.g., customer key) to collocate joins with dimension tables, while dimension tables are replicated across nodes to avoid data movement. Columnstore indexes compress fact tables by column, drastically reducing storage for repetitive values like dates or product IDs, which is why star schemas achieve minimal redundancy. A real-world scenario: a retail company using a star schema can run a year-over-year sales trend query by simply filtering the date dimension, without scanning redundant customer names stored in the fact table.
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 core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Star schema with dimension and fact tables — The star schema is the correct choice because it separates business processes into fact tables (for measures like sales quantities) and dimension tables (for descriptive attributes like customer or date). This design directly supports both historical trend analysis (by joining facts with the date dimension) and current-day reporting (by filtering on the latest date) while minimizing storage redundancy through normalized dimensions. Azure Synapse Analytics is optimized for star schemas, leveraging columnstore indexes and distributed tables to accelerate such queries.
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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Question Discussion
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