Question 140 of 982

Describe an analytics workload on Azure →hardMultiple 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 manufacturing company ingests a continuous stream of sensor data from factory equipment into Azure Event Hubs. Additionally, historical maintenance data in CSV format is stored in Azure Data Lake Storage Gen2. The analytics team needs to join the streaming sensor data with the historical data in near real-time and enable analysts to query the combined dataset using standard T-SQL without moving the data. Which Azure service should they use as the primary analytics platform?

Clue words in this question

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

Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

Question 1hardmultiple choice

Full question →

A
A) Azure Stream Analytics
Why wrong: Incorrect. While Azure Stream Analytics can process streaming data and output to various sinks, it does not directly support joining streaming data with batch data or allow ad-hoc T-SQL queries on the combined dataset from the data lake.
B
B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool
Correct. Synapse Pipelines can orchestrate the ingestion of both streaming (Event Hubs) and batch (Data Lake) data. The data can be stored in the lake, and the serverless SQL pool can query it using T-SQL, providing a unified analytics surface without moving data. This meets all requirements.
C
C) Azure SQL Database
Why wrong: Incorrect. Azure SQL Database is a relational database designed for OLTP workloads. It cannot directly query data stored in Azure Data Lake Storage Gen2 or join streaming data from Event Hubs without complex external tables or data movement.
D
D) Azure Databricks
Why wrong: Incorrect. Azure Databricks is a Spark-based analytics platform that can process streaming and batch data, but it does not offer native T-SQL query capability. Users would need to use Spark SQL or Python/Scala, not standard T-SQL as required.

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 Synapse Analytics with Synapse Pipelines and serverless SQL pool

Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool is the correct choice because it can ingest streaming data from Event Hubs via pipelines, query historical CSV data in Data Lake Storage Gen2 directly using T-SQL without moving it, and join both datasets in near real-time using the serverless SQL pool's ability to reference external data sources. This meets the requirement for standard T-SQL queries on combined streaming and historical data without data movement.

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 Stream Analytics
Why it's wrong here
Incorrect. While Azure Stream Analytics can process streaming data and output to various sinks, it does not directly support joining streaming data with batch data or allow ad-hoc T-SQL queries on the combined dataset from the data lake.
✓
B) Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool
Why this is correct
Correct. Synapse Pipelines can orchestrate the ingestion of both streaming (Event Hubs) and batch (Data Lake) data. The data can be stored in the lake, and the serverless SQL pool can query it using T-SQL, providing a unified analytics surface without moving data. This meets all requirements.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
C) Azure SQL Database
Why it's wrong here
Incorrect. Azure SQL Database is a relational database designed for OLTP workloads. It cannot directly query data stored in Azure Data Lake Storage Gen2 or join streaming data from Event Hubs without complex external tables or data movement.
✗
D) Azure Databricks
Why it's wrong here
Incorrect. Azure Databricks is a Spark-based analytics platform that can process streaming and batch data, but it does not offer native T-SQL query capability. Users would need to use Spark SQL or Python/Scala, not standard T-SQL as required.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose Azure Stream Analytics because it is the most obvious service for streaming data, but they overlook the requirement for standard T-SQL queries on combined datasets without data movement, which Stream Analytics cannot fulfill as it uses its own query language and cannot directly join with static data in Data Lake Storage Gen2 using T-SQL.

Trap categories for this question

Command / output trap
Incorrect. While Azure Stream Analytics can process streaming data and output to various sinks, it does not directly support joining streaming data with batch data or allow ad-hoc T-SQL queries on the combined dataset from the data lake.

Detailed technical explanation

How to think about this question

The serverless SQL pool in Azure Synapse uses a distributed query engine that can read data directly from Data Lake Storage Gen2 via OPENROWSET or external tables, supporting T-SQL syntax for joins with streaming data ingested through Event Hubs and staged in a landing zone. This architecture leverages PolyBase-like technology to pushdown predicates and filter data at the storage layer, enabling near real-time analytics without ETL overhead. In practice, this allows analysts to run queries like 'SELECT * FROM streaming_data JOIN historical_data ON sensor_id = device_id' using standard T-SQL, with the streaming data buffered in a staging table or external data source.

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 Synapse Analytics with Synapse Pipelines and serverless SQL pool — Azure Synapse Analytics with Synapse Pipelines and serverless SQL pool is the correct choice because it can ingest streaming data from Event Hubs via pipelines, query historical CSV data in Data Lake Storage Gen2 directly using T-SQL without moving it, and join both datasets in near real-time using the serverless SQL pool's ability to reference external data sources. This meets the requirement for standard T-SQL queries on combined streaming and historical data without data movement.

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: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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