Question 872 of 982

Describe an analytics workload on Azure →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is Azure Databricks. This service is correct because it offers a serverless, interactive Apache Spark environment where data scientists can perform interactive data exploration with Azure Databricks notebooks using Python, directly querying raw JSON files in Azure Data Lake Storage Gen2. It automatically scales compute resources and minimizes administrative overhead by managing cluster lifecycles, while also allowing the BI team to produce aggregated datasets for reporting. On the DP-900 exam, this scenario tests your understanding of which Azure service handles both ad-hoc exploratory analytics and downstream reporting without provisioning infrastructure—a common trap is choosing Azure Synapse Analytics, which is more suited for large-scale data warehousing and pipelines rather than interactive notebook-based exploration. Remember the memory tip: “Databricks for discovery, Synapse for delivery”—if the task starts with raw data and a notebook, think Databricks first.

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 ingests raw clickstream data as JSON files into Azure Data Lake Storage Gen2. Data scientists need to explore the data interactively using Python notebooks, and the BI team needs to create reports from aggregated datasets derived from this data. The solution must be serverless, scale automatically, and minimize administration. Which Azure service should they choose?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1hardmultiple choice

Study the full Python automation breakdown →

A
A. Azure Synapse Analytics (serverless SQL pool)
Why wrong: Serverless SQL pool is great for SQL-based queries on data lakes but does not natively support Python notebook development for interactive exploration by data scientists. It is more SQL-focused.
B
B. Azure Databricks
Azure Databricks provides collaborative notebooks with Python, Scala, and SQL support. Its serverless mode pools resources dynamically, scales automatically, and handles the full data science lifecycle from exploration to transformation.
C
C. Azure HDInsight with Spark
Why wrong: HDInsight offers Spark clusters but requires manual cluster management and provisioning. It is not serverless, and the administration overhead is higher, which contradicts the requirement to minimize administration.
D
D. Azure Data Lake Analytics
Why wrong: Data Lake Analytics uses U-SQL, not Python notebooks, and is a batch-oriented service. It does not provide an interactive notebook environment suitable for data scientists exploring data.

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

✓

B. Azure Databricks

Azure Databricks is correct because it provides a serverless, interactive Apache Spark environment that data scientists can use with Python notebooks for exploratory analysis, and it can produce aggregated datasets for BI reporting. It scales automatically and minimizes administration by managing the cluster lifecycle, making it ideal for ad-hoc data exploration on raw JSON files in Azure Data Lake Storage Gen2.

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.

✗
A. Azure Synapse Analytics (serverless SQL pool)
Why it's wrong here
Serverless SQL pool is great for SQL-based queries on data lakes but does not natively support Python notebook development for interactive exploration by data scientists. It is more SQL-focused.
✓
B. Azure Databricks
Why this is correct
Azure Databricks provides collaborative notebooks with Python, Scala, and SQL support. Its serverless mode pools resources dynamically, scales automatically, and handles the full data science lifecycle from exploration to transformation.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
C. Azure HDInsight with Spark
Why it's wrong here
HDInsight offers Spark clusters but requires manual cluster management and provisioning. It is not serverless, and the administration overhead is higher, which contradicts the requirement to minimize administration.
✗
D. Azure Data Lake Analytics
Why it's wrong here
Data Lake Analytics uses U-SQL, not Python notebooks, and is a batch-oriented service. It does not provide an interactive notebook environment suitable for data scientists exploring data.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse serverless SQL pools (Synapse) as suitable for interactive Python exploration, but they are designed for SQL-based querying, not notebook-based data science workflows.

Detailed technical explanation

How to think about this question

Azure Databricks leverages Apache Spark clusters that can be configured to auto-terminate after inactivity, ensuring cost efficiency while providing a collaborative workspace with Delta Lake for ACID transactions on the raw JSON data. Under the hood, it uses a managed Spark engine with optimized I/O to Azure Data Lake Storage Gen2 via the ABFS driver, enabling fast reads of semi-structured data. In a real-world scenario, data scientists can use Databricks notebooks to perform ad-hoc transformations and write aggregated results back as Delta tables, which the BI team can then query with Power BI directly.

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: B. Azure Databricks — Azure Databricks is correct because it provides a serverless, interactive Apache Spark environment that data scientists can use with Python notebooks for exploratory analysis, and it can produce aggregated datasets for BI reporting. It scales automatically and minimizes administration by managing the cluster lifecycle, making it ideal for ad-hoc data exploration on raw JSON files in Azure Data Lake Storage Gen2.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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