The most effective solution is to repartition the Parquet files by both date and CustomerID, and optimize file sizes to about 1 GB each. This works because Azure Synapse Serverless SQL pool can perform partition pruning when queries filter on these columns, allowing the engine to skip irrelevant partitions and scan only the necessary data—drastically reducing the 10 TB of data read per query. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of how serverless SQL pools rely on external data layout for performance, since you cannot create indexes or materialized views. A common trap is assuming that partitioning by date alone is sufficient, but adding CustomerID enables more precise pruning for ad-hoc customer lookups. Remember the memory tip: “Partition by what you filter, and size files to a gig.”
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. 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 a data engineer for a large e-commerce company. The company uses Azure Data Lake Storage Gen2 to store customer transaction data. They also use Azure Databricks for data transformation and Azure Synapse Serverless SQL pool for ad-hoc queries. Recently, the data lake has grown to 10 TB, and query performance in Synapse Serverless has degraded significantly. Users complain that queries that used to take seconds now take minutes. You need to improve query performance without moving data to a dedicated SQL pool. The data is stored in Parquet format, partitioned by date. You notice that the queries often filter on CustomerID and Date. Current queries scan all partitions even when only a few days are needed. What is the most effective solution to improve performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Repartition the Parquet files by both date and CustomerID, and optimize file sizes to 1 GB each
Option C is correct because repartitioning the Parquet files by both date and CustomerID enables partition pruning in Azure Synapse Serverless SQL pool. When queries filter on CustomerID and Date, the engine can skip irrelevant partitions entirely, drastically reducing the amount of data scanned. Optimizing file sizes to around 1 GB ensures efficient parallelism and avoids the overhead of many small files, which degrades performance in a serverless environment.
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.
✗
Create materialized views in the Serverless SQL database on the partitioned data
Why it's wrong here
Serverless SQL does not support materialized views; it queries external data directly.
✗
Convert all Parquet files to CSV and use row-level security to limit data access
Why it's wrong here
CSV is not optimized for analytics and would worsen performance.
✓
Repartition the Parquet files by both date and CustomerID, and optimize file sizes to 1 GB each
Why this is correct
Partition pruning will eliminate irrelevant partitions, reducing data scanned.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the service level of the Synapse workspace to improve query concurrency
Why it's wrong here
Service level affects concurrency and resources, but not the amount of data scanned.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think materialized views (Option A) or scaling up (Option D) can fix performance issues caused by poor data partitioning, but they overlook that serverless SQL pools rely heavily on data layout and partition pruning for efficient query execution.
Detailed technical explanation
How to think about this question
Parquet is a columnar storage format that supports predicate pushdown and partition pruning at the file level. By repartitioning on both date and CustomerID, the Synapse Serverless SQL pool can use the partition elimination feature to read only the relevant folders, reducing I/O. The 1 GB file size recommendation aligns with Azure Synapse best practices to balance parallelism and metadata overhead; files that are too small cause excessive listing operations, while files that are too large limit parallelism.
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: Repartition the Parquet files by both date and CustomerID, and optimize file sizes to 1 GB each — Option C is correct because repartitioning the Parquet files by both date and CustomerID enables partition pruning in Azure Synapse Serverless SQL pool. When queries filter on CustomerID and Date, the engine can skip irrelevant partitions entirely, drastically reducing the amount of data scanned. Optimizing file sizes to around 1 GB ensures efficient parallelism and avoids the overhead of many small files, which degrades performance in a serverless environment.
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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