Question 161 of 982

Describe an analytics workload on Azure →easyMultiple ChoiceObjective-mapped

Quick Answer

The answer is Azure Synapse Analytics with Apache Spark pools. This service is correct because it provides a fully managed, serverless Spark environment that natively supports Python and Apache Spark for large-scale data transformations and machine learning experiments, while integrating directly with Azure Data Lake Storage Gen2 for seamless data access. On the DP-900 exam, this question tests your understanding of which Azure analytics service is purpose-built for big data and ML workloads using Spark, often contrasting it with Azure Databricks or Azure Machine Learning—a common trap is confusing Synapse’s Spark pools with Databricks, but remember that Synapse is the native Azure solution for unified data warehousing and big data analytics. A helpful memory tip: think “Synapse Spark = Synapse + Spark” for large-scale, collaborative Jupyter notebook workloads on ADLS Gen2.

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 company stores weather sensor data in Azure Data Lake Storage Gen2. Data scientists need to run large-scale transformations and machine learning experiments on this data using Python and Apache Spark. They want to collaborate using shared Jupyter notebooks. Which Azure service should they use for this analytical workload?

Question 1easymultiple choice

Study the full Python automation breakdown →

A
Azure Stream Analytics
Why wrong: Azure Stream Analytics is designed for real-time stream processing, not interactive Spark-based analytics and machine learning on historical data.
B
Azure Synapse Analytics (with Apache Spark pools)
Correct. Azure Synapse Analytics provides Apache Spark pools integrated with Jupyter notebooks, enabling data scientists to run Python/Spark jobs on data stored in ADLS Gen2 for transformations and ML.
C
Azure Analysis Services
Why wrong: Azure Analysis Services is a semantic modeling engine for OLAP and BI, not designed for running Spark or Python-based data transformations.
D
Azure SQL Database
Why wrong: Azure SQL Database is a relational OLTP database, not suited for large-scale data transformations with Apache Spark or Python.

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 (with Apache Spark pools)

Azure Synapse Analytics with Apache Spark pools provides a fully managed Spark environment that supports Python and allows data scientists to run large-scale transformations and machine learning experiments. It integrates directly with Azure Data Lake Storage Gen2 for reading and writing data, and supports collaborative Jupyter notebooks for shared development. This makes it the correct choice for the described analytical workload.

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 Stream Analytics
Why it's wrong here
Azure Stream Analytics is designed for real-time stream processing, not interactive Spark-based analytics and machine learning on historical data.
✓
Azure Synapse Analytics (with Apache Spark pools)
Why this is correct
Correct. Azure Synapse Analytics provides Apache Spark pools integrated with Jupyter notebooks, enabling data scientists to run Python/Spark jobs on data stored in ADLS Gen2 for transformations and ML.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is a semantic modeling engine for OLAP and BI, not designed for running Spark or Python-based data transformations.
✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational OLTP database, not suited for large-scale data transformations with Apache Spark or Python.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may confuse Azure Stream Analytics (a real-time processing service) with batch processing, or think Azure SQL Database can handle large-scale Spark workloads, when in fact only Synapse Analytics with Spark pools provides the required Python, Spark, and collaborative notebook capabilities.

Detailed technical explanation

How to think about this question

Azure Synapse Analytics unifies big data and data warehousing, and its Apache Spark pools are based on managed Spark clusters that can auto-scale and support Delta Lake for ACID transactions on Data Lake Storage Gen2. The integration with Jupyter notebooks allows multiple data scientists to collaborate in real time, sharing code and results via the Synapse Studio interface. In a real-world scenario, a team could ingest terabytes of sensor data from Data Lake Storage Gen2, perform feature engineering using PySpark, and train models using Spark MLlib, all within the same notebook environment.

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.

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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 (with Apache Spark pools) — Azure Synapse Analytics with Apache Spark pools provides a fully managed Spark environment that supports Python and allows data scientists to run large-scale transformations and machine learning experiments. It integrates directly with Azure Data Lake Storage Gen2 for reading and writing data, and supports collaborative Jupyter notebooks for shared development. This makes it the correct choice for the described analytical workload.

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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