mediumMultiple SelectObjective-mapped
Optimize Azure Databricks ETL Performance with Delta Lake and Photon
You are designing a data transformation pipeline using Azure Databricks. The pipeline reads from Azure Data Lake Storage Gen2, performs aggregations, and writes to a Synapse dedicated SQL pool. Which three configurations should you implement to optimize performance and minimize cost? (Choose three.)
Quick Answer
The answer is to enable the Photon engine for Spark SQL operations, use Delta Lake for ACID transactions and scalable metadata handling, and configure auto-scaling for the Databricks cluster. These three configurations directly optimize Azure Databricks ETL performance by accelerating query execution through vectorized engine processing, ensuring reliable data transformations with Delta Lake’s schema enforcement and time travel, and minimizing cost by dynamically adjusting cluster resources based on workload demands. On the DP-203 exam, this scenario tests your ability to balance performance and cost in a real-world pipeline reading from ADLS Gen2 and writing to Synapse; a common trap is selecting manual cluster sizing instead of auto-scaling, which wastes resources during idle periods. Remember the mnemonic “PAD” for Photon, Auto-scaling, and Delta—three pillars that power efficient ETL in Azure Databricks.
⚠ Common exam trap
Many candidates assume cost savings come from reducing cluster size (single-node) or disabling autoscaling, but in practice these choices hurt performance and can increase total cost due to longer runtimes and resource contention.
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
✓
Enable Delta Lake on the storage account
Enabling Delta Lake on the storage account allows you to use Delta tables, which provide ACID transactions, scalable metadata handling, and unified batch/streaming capabilities. This is essential for reliable and performant data transformations in Azure Databricks, especially when reading from ADLS Gen2 and writing to Synapse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a single-node cluster to reduce cost
Why it's wrong here
Single-node cluster cannot handle large data volumes efficiently.
- ✗
Disable autoscaling to avoid cost variability
Why it's wrong here
Autoscaling helps minimize cost by scaling down during idle periods.
- ✗
Use default Spark shuffle partitions (200)
Why it's wrong here
Default may not be optimal; tuning shuffle partitions is recommended.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-203 exam frequently reuses these exact scenarios with slightly different constraints.
✓Enable Delta Lake on the storage accountCorrect answer▾
✗Use a single-node cluster to reduce costWrong answer — click to see why▾
Why this is wrong here
Single-node cluster cannot handle large data volumes efficiently.
✗Disable autoscaling to avoid cost variabilityWrong answer — click to see why▾
Why this is wrong here
Autoscaling helps minimize cost by scaling down during idle periods.
✗Use default Spark shuffle partitions (200)Wrong answer — click to see why▾
Why this is wrong here
Default may not be optimal; tuning shuffle partitions is recommended.
Analysis generated from the official DP-203blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
About these practice questions
Courseiva writes every DP-203 question from scratch — 760 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more way this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer needs to process a large dataset stored in Azure Blob Storage using Azure Databricks. The dataset consists of millions of small CSV files. The processing job is slow due to the overhead of reading many small files. Which technique should be used to improve performance?
easy- A.Increase the number of worker nodes in the cluster
- B.Convert the CSV files to Parquet format
- ✓ C.Coalesce the small files into larger files using a Databricks notebook
- D.Use Delta Lake caching to store the data in memory
Why C: Coalescing the millions of small CSV files into larger files reduces the metadata overhead and I/O operations when reading from Azure Blob Storage. Databricks can then process fewer, larger files more efficiently, as each task handles a substantial data chunk rather than incurring the cost of opening and closing many small files.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.