DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
A company uses Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage Gen2. They notice that queries are slow and want to improve performance by reducing the amount of data read. What is the most effective strategy?
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 data by a frequently filtered column and use file elimination in queries.
Partitioning the data in the lake and using partition elimination reduces data read. Option B is wrong because increasing the number of compute nodes is not possible in serverless; it's auto-scaling. Option C is wrong because OPENROWSET with CSV reads all files; it does not reduce data read. Option D is wrong because CETAS is for creating external tables, not for improving query performance directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the data by a frequently filtered column and use file elimination in queries.
Why this is correct
Partitioning the data in the lake and using partition elimination reduces data read.
- ✗
Increase the number of compute nodes in the serverless pool.
Why it's wrong here
Increasing the number of compute nodes is not possible in serverless; it's auto-scaling.
- ✗
Use OPENROWSET with CSV format instead of Parquet.
Why it's wrong here
OPENROWSET with CSV reads all files; it does not reduce data read.
- ✗
Create external tables using CETAS and query them.
Why it's wrong here
CETAS is for creating external tables, not for improving query performance directly.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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