The answer is Parquet, as it is the optimal data format for querying large datasets in Azure Blob Storage with serverless SQL pool in Azure Synapse. Parquet is a columnar storage format that compresses data efficiently and supports predicate pushdown, meaning the serverless SQL pool reads only the necessary columns and rows rather than scanning entire files. This drastically reduces the amount of data processed, minimizing storage costs while maintaining high query performance—unlike row-oriented formats like CSV or JSON, which require full scans and consume more resources. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of how data format choices directly impact cost and performance in Azure Synapse serverless SQL pools. A common trap is assuming CSV is cheaper because it’s simpler, but Parquet’s compression and columnar structure actually lower both storage and query costs. Memory tip: think “Parquet = Parallel + Compact” for fast, cost-efficient columnar queries.
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.
A data analyst needs to query a large dataset stored in Azure Blob Storage using serverless SQL pool in Azure Synapse Analytics. Which data format should they use to minimize storage costs while still supporting efficient querying?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Parquet
Parquet is a columnar storage format that compresses data efficiently and supports predicate pushdown, allowing serverless SQL pool in Azure Synapse to read only the necessary columns and rows. This minimizes storage costs while maintaining high query performance, unlike row-oriented formats such as CSV or JSON.
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.
✗
CSV
Why it's wrong here
CSV is row-based and does not compress as well; it is less efficient for analytical workloads.
✗
JSON
Why it's wrong here
JSON is verbose and lacks schema enforcement, leading to larger storage and slower queries.
✓
Parquet
Why this is correct
Parquet is a columnar format that offers high compression and efficient query performance, minimizing storage costs.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Avro
Why it's wrong here
Avro is row-based and better for write-heavy or streaming scenarios, not optimized for read-heavy analytics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume all compressed formats (like Avro) are equally efficient for analytics, but Azure Synapse serverless SQL pool is specifically optimized for columnar formats like Parquet, not row-oriented ones.
Trap categories for this question
Scenario analysis trap
Avro is row-based and better for write-heavy or streaming scenarios, not optimized for read-heavy analytics.
Detailed technical explanation
How to think about this question
Parquet uses columnar storage with built-in compression algorithms like Snappy or GZip, and stores statistics (min, max, null counts) per row group, enabling serverless SQL pool to skip irrelevant data via partition elimination and predicate pushdown. In real-world scenarios, a 1 TB CSV dataset can shrink to under 100 GB in Parquet, drastically reducing both storage costs and query execution time for analytical workloads.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Parquet — Parquet is a columnar storage format that compresses data efficiently and supports predicate pushdown, allowing serverless SQL pool in Azure Synapse to read only the necessary columns and rows. This minimizes storage costs while maintaining high query performance, unlike row-oriented formats such as CSV or JSON.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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