Question 90 of 982

Describe an analytics workload on Azure →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to add Azure Cosmos DB as an additional output from Azure Stream Analytics for real-time dashboards, enable Azure Synapse Link on the Cosmos DB container to continuously replicate data to Azure Synapse Analytics for historical queries, and remove the real-time dashboard load from Azure SQL Database. This works because Azure Stream Analytics supports multiple sinks simultaneously, allowing you to split the workload: Cosmos DB handles low-latency real-time writes for dashboards, while Synapse Link automatically replicates the same data into Azure Synapse Analytics for sub-second queries on years of historical data, all without impacting the write performance of the original SQL Database. On the DP-900 exam, this scenario tests your understanding of how to redesign analytics workloads for real-time and historical needs using Cosmos DB and Synapse Link, often appearing as a trap where candidates mistakenly try to fix SQL Database performance or add batch processing. A common memory tip is "Stream to Cosmos for speed, Synapse Link for history"—think of Cosmos as the hot path for live dashboards and Synapse Link as the cold path for deep historical analysis.

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.

Your company operates a retail analytics platform. Data from point-of-sale systems is ingested in real time into Azure Event Hubs. The data is then consumed by an Azure Stream Analytics job that aggregates sales by store and product every minute, writing results to Azure SQL Database. The business now requires a historical trend analysis capability that can query the last three years of sales data with sub-second response times, but the SQL Database is already experiencing performance issues due to high write volume. You need to redesign the serving layer to support both real-time dashboards (seconds latency) and historical analytics (sub-second queries on years of data) without impacting write performance. What should you do?

Question 1hardmultiple choice

Full question →

A
Modify the Azure Stream Analytics job to write to Azure Data Lake Storage Gen2, then use Azure Databricks to run batch transformations and load into a separate Azure Synapse Analytics dedicated SQL pool for historical queries. Keep the real-time dashboard connected to Azure SQL Database.
Why wrong: Batch processing adds latency, not sub-second response for historical queries.
B
Add Azure Cosmos DB as an additional output from Azure Stream Analytics for real-time dashboards. Enable Azure Synapse Link on the Cosmos DB container to continuously replicate data to Azure Synapse Analytics for historical queries. Remove the real-time dashboard load from Azure SQL Database.
Cosmos DB provides low latency for real-time dashboards, and Synapse Link enables near real-time analytics on historical data without impacting writes.
C
Add Azure Analysis Services on top of Azure SQL Database and create a cube for historical queries. Use Azure Stream Analytics to write to Analysis Services directly.
Why wrong: Analysis Services is not designed for sub-second queries on raw historical data; also direct writes are not supported.
D
Partition the existing Azure SQL Database and add columnstore indexes to improve query performance. Keep the current architecture and increase the DTU/vCore limit to handle both workloads.
Why wrong: This might not solve the write performance issue and still could degrade under high load.

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

✓

Add Azure Cosmos DB as an additional output from Azure Stream Analytics for real-time dashboards. Enable Azure Synapse Link on the Cosmos DB container to continuously replicate data to Azure Synapse Analytics for historical queries. Remove the real-time dashboard load from Azure SQL Database.

Option C is correct. Azure Stream Analytics can output to multiple sinks simultaneously. By adding Azure Cosmos DB as an additional output for real-time dashboards (low latency, high throughput), and using Azure Synapse Link to continuously replicate Cosmos DB data to Azure Synapse Analytics for historical queries, you separate workloads. Option A uses Azure Analysis Services but it is not designed for sub-second queries on raw historical data. Option B uses Azure Data Lake Storage with Databricks, but batch processing would not meet the real-time requirement and adds latency. Option D uses Azure SQL Database with indexing improvements, but the write volume already causes issues, and SQL DB is not ideal for sub-second queries on years of data without partitioning and scaling.

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.

✗
Modify the Azure Stream Analytics job to write to Azure Data Lake Storage Gen2, then use Azure Databricks to run batch transformations and load into a separate Azure Synapse Analytics dedicated SQL pool for historical queries. Keep the real-time dashboard connected to Azure SQL Database.
Why it's wrong here
Batch processing adds latency, not sub-second response for historical queries.
✓
Add Azure Cosmos DB as an additional output from Azure Stream Analytics for real-time dashboards. Enable Azure Synapse Link on the Cosmos DB container to continuously replicate data to Azure Synapse Analytics for historical queries. Remove the real-time dashboard load from Azure SQL Database.
Why this is correct
Cosmos DB provides low latency for real-time dashboards, and Synapse Link enables near real-time analytics on historical data without impacting writes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add Azure Analysis Services on top of Azure SQL Database and create a cube for historical queries. Use Azure Stream Analytics to write to Analysis Services directly.
Why it's wrong here
Analysis Services is not designed for sub-second queries on raw historical data; also direct writes are not supported.
✗
Partition the existing Azure SQL Database and add columnstore indexes to improve query performance. Keep the current architecture and increase the DTU/vCore limit to handle both workloads.
Why it's wrong here
This might not solve the write performance issue and still could degrade under high load.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 DP-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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: Add Azure Cosmos DB as an additional output from Azure Stream Analytics for real-time dashboards. Enable Azure Synapse Link on the Cosmos DB container to continuously replicate data to Azure Synapse Analytics for historical queries. Remove the real-time dashboard load from Azure SQL Database. — Option C is correct. Azure Stream Analytics can output to multiple sinks simultaneously. By adding Azure Cosmos DB as an additional output for real-time dashboards (low latency, high throughput), and using Azure Synapse Link to continuously replicate Cosmos DB data to Azure Synapse Analytics for historical queries, you separate workloads. Option A uses Azure Analysis Services but it is not designed for sub-second queries on raw historical data. Option B uses Azure Data Lake Storage with Databricks, but batch processing would not meet the real-time requirement and adds latency. Option D uses Azure SQL Database with indexing improvements, but the write volume already causes issues, and SQL DB is not ideal for sub-second queries on years of data without partitioning and scaling.

What should I do if I get this DP-900 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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