Question 982 of 982

Describe an analytics workload on Azure →mediumMultiple ChoiceObjective-mapped

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.

A data engineering team needs to build a batch ETL pipeline that transforms large volumes of clickstream data stored as CSV files in Azure Data Lake Storage Gen2. The transformations require running distributed Python and Scala code using Apache Spark. The transformed data will be loaded into a data warehouse for reporting. The team wants a serverless compute environment that automatically scales and charges per second. Which Azure service should they use to run the Spark transformations?

Question 1mediummultiple choice

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A
Azure Synapse Analytics (Spark pools)
Azure Synapse Analytics provides serverless and dedicated Spark pools that can run distributed Spark jobs on data in ADLS Gen2. It integrates tightly with the data lake and offers per-second billing for serverless pools.
B
Azure Data Factory
Why wrong: Data Factory is an orchestration service for data movement and transformation. It does not run Spark code directly; it can invoke external compute, but it is not the compute environment for Spark transformations.
C
Azure Stream Analytics
Why wrong: Stream Analytics is for real-time stream processing, not batch ETL with Spark. It uses a SQL-like language, not Python or Scala.
D
Azure Analysis Services
Why wrong: Analysis Services is for creating semantic data models and does not provide Spark compute capabilities.

Full breakdown with real-world context →

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

✓

Azure Synapse Analytics (Spark pools)

Azure Synapse Analytics (Spark pools) is the correct choice because it provides a serverless Apache Spark compute environment that automatically scales and charges per second, perfectly matching the requirement for running distributed Python and Scala transformations on large volumes of clickstream data stored in Azure Data Lake Storage Gen2. The service integrates directly with the data lake and can load transformed results into a dedicated SQL pool for data warehouse reporting.

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.

✓
Azure Synapse Analytics (Spark pools)
Why this is correct
Azure Synapse Analytics provides serverless and dedicated Spark pools that can run distributed Spark jobs on data in ADLS Gen2. It integrates tightly with the data lake and offers per-second billing for serverless pools.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
Data Factory is an orchestration service for data movement and transformation. It does not run Spark code directly; it can invoke external compute, but it is not the compute environment for Spark transformations.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is for real-time stream processing, not batch ETL with Spark. It uses a SQL-like language, not Python or Scala.
✗
Azure Analysis Services
Why it's wrong here
Analysis Services is for creating semantic data models and does not provide Spark compute capabilities.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse Azure Data Factory's ability to orchestrate Spark jobs with actually running Spark code, leading them to select it instead of recognizing that Synapse Spark pools are the dedicated compute service for executing distributed Python/Scala transformations.

Detailed technical explanation

How to think about this question

Azure Synapse Spark pools use Apache Spark as the execution engine, with each pool consisting of a driver node and worker nodes that can be scaled from 3 to 200 nodes. The serverless model means no infrastructure management is needed; the pool starts in about 2-5 minutes and automatically scales based on the workload, with billing at $0.07 per vCore-hour (as of typical pricing). A real-world scenario involves processing terabytes of clickstream CSV files partitioned by date, where Spark's lazy evaluation and in-memory computation reduce shuffle overhead compared to traditional MapReduce.

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.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-900 question test?

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: Azure Synapse Analytics (Spark pools) — Azure Synapse Analytics (Spark pools) is the correct choice because it provides a serverless Apache Spark compute environment that automatically scales and charges per second, perfectly matching the requirement for running distributed Python and Scala transformations on large volumes of clickstream data stored in Azure Data Lake Storage Gen2. The service integrates directly with the data lake and can load transformed results into a dedicated SQL pool for data warehouse reporting.

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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