DP-203 Partition pruning Practice Question
You are designing a data processing solution using Azure Synapse Analytics serverless SQL pool. The solution will query data stored in Parquet files in Azure Data Lake Storage Gen2. You need to ensure that the queries are optimized for performance. Which action 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
✓
Partition the Parquet files by date and use partition pruning in the query.
Partitioning Parquet files by a commonly filtered column, such as date, allows Azure Synapse serverless SQL pool to perform partition pruning, which eliminates scanning unnecessary partitions and reduces the amount of data read. Option A is incorrect because increasing MAXDOP (maximum degree of parallelism) can lead to resource contention and may not improve query performance in serverless SQL pool. Option B is incorrect because Parquet is a columnar format optimized for analytics and is more efficient than CSV for querying large datasets. Option C is incorrect because materialized views are not supported in serverless SQL pool; they are only available in dedicated SQL pool.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the MAXDOP setting in the query.
Why it's wrong here
Increasing MAXDOP may cause resource contention and does not improve performance for partition pruning.
- ✗
Convert the Parquet files to CSV format for faster parsing.
Why it's wrong here
Converting to CSV would degrade performance because CSV is row-based and slower for analytical queries compared to columnar Parquet.
- ✗
Create materialized views on the external tables.
Why it's wrong here
Materialized views are not supported in serverless SQL pool; they are a feature of dedicated SQL pool.
- ✓
Partition the Parquet files by date and use partition pruning in the query.
Why this is correct
Partitioning by date enables partition pruning, reducing data scanned and improving query performance.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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