Question 734 of 982

Describe an analytics workload on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is Azure Synapse Analytics serverless Spark pool. This service is the correct choice because it provides a fully serverless Apache Spark compute environment that automatically scales based on workload demand, eliminating the need for any cluster management or provisioning. For a data engineer transforming large datasets stored in Azure Data Lake Storage Gen2 using Python and Spark, this serverless pool directly integrates with the storage layer, allowing on-demand compute that spins up and down as jobs run. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of the difference between serverless and provisioned compute options within Azure Synapse Analytics. A common trap is confusing this with Azure Databricks, which also offers serverless Spark but requires more configuration and is not the native serverless pool tied directly to Synapse workspaces. Remember the memory tip: "Synapse serverless Spark = no cluster to park, just point and spark."

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 engineer needs to transform large datasets stored in Azure Data Lake Storage Gen2 using Python and Apache Spark. They want a serverless compute option that automatically scales and requires no cluster management. Which Azure service should they use?

Question 1mediummultiple choice

Study the full Python automation breakdown →

A
Azure Synapse Analytics dedicated SQL pool
Why wrong: Dedicated SQL pool is for relational data warehousing with provisioned compute, not for running Spark jobs.
B
Azure Databricks with interactive clusters
Why wrong: While Azure Databricks can run Spark jobs, interactive clusters require manual startup/stop and management. Serverless pools in Synapse are even more hands-off.
C
Azure Synapse Analytics serverless Spark pool
Correct. Serverless Spark pools in Azure Synapse Analytics are fully managed, automatically scale, and charge only for active compute. Ideal for ad-hoc transformations without cluster management.
D
Azure Data Factory with MapReduce
Why wrong: Azure Data Factory is an orchestration service and MapReduce is a legacy processing model; it does not provide a serverless Spark environment.

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 serverless Spark pool

Azure Synapse Analytics serverless Spark pool is correct because it provides a serverless Apache Spark compute environment that automatically scales based on workload demand and requires no cluster management. This aligns perfectly with the requirement to transform large datasets in Azure Data Lake Storage Gen2 using Python and Spark without provisioning or managing infrastructure.

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 dedicated SQL pool
Why it's wrong here
Dedicated SQL pool is for relational data warehousing with provisioned compute, not for running Spark jobs.
✗
Azure Databricks with interactive clusters
Why it's wrong here
While Azure Databricks can run Spark jobs, interactive clusters require manual startup/stop and management. Serverless pools in Synapse are even more hands-off.
✓
Azure Synapse Analytics serverless Spark pool
Why this is correct
Correct. Serverless Spark pools in Azure Synapse Analytics are fully managed, automatically scale, and charge only for active compute. Ideal for ad-hoc transformations without cluster management.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory with MapReduce
Why it's wrong here
Azure Data Factory is an orchestration service and MapReduce is a legacy processing model; it does not provide a serverless Spark environment.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse 'serverless' with 'interactive clusters' in Azure Databricks, assuming that Databricks offers a serverless option (which it does not for interactive clusters), or they mistakenly think a dedicated SQL pool can run Spark transformations.

Detailed technical explanation

How to think about this question

Under the hood, Azure Synapse serverless Spark pools use a decoupled compute-storage architecture where Spark executors are dynamically allocated from a shared pool of resources, scaling from 0 to the configured maximum based on the number of tasks. This is achieved through Apache Spark's dynamic allocation feature combined with Azure's infrastructure, allowing jobs to start in seconds without any cluster startup delay. A real-world scenario is a data engineer running ad-hoc data transformation jobs on terabytes of Parquet files in ADLS Gen2, where the serverless pool spins up only the needed executors and shuts them down after job completion, minimizing costs.

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 serverless Spark pool — Azure Synapse Analytics serverless Spark pool is correct because it provides a serverless Apache Spark compute environment that automatically scales based on workload demand and requires no cluster management. This aligns perfectly with the requirement to transform large datasets in Azure Data Lake Storage Gen2 using Python and Spark without provisioning or managing infrastructure.

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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