- A
Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
Correct. ADF orchestrates the pipeline, Databricks performs Spark-based transformations, and Synapse Analytics serves as the data warehouse for reporting.
- B
Azure Stream Analytics, Azure Data Lake Storage, and Power BI.
Why wrong: Stream Analytics is for real-time stream processing, not scheduled batch processing of CSV files. Power BI is a visualization tool, not a transformation or storage engine.
- C
Azure Functions, Azure SQL Database, and Azure Analysis Services.
Why wrong: Azure Functions is suitable for small-scale event-driven processing, not complex Spark transformations. Azure SQL Database is not designed for large-scale data warehousing workloads, and Analysis Services is a semantic model layer, not a data warehouse.
- D
Azure Logic Apps, Azure HDInsight, and Azure Cosmos DB.
Why wrong: Logic Apps can orchestrate but lacks deep integration with Spark. Azure HDInsight is a managed Hadoop/Spark service but is less integrated than Databricks with ADF. Cosmos DB is a NoSQL database, not suitable for large-scale analytical queries like those run in Synapse.
Quick Answer
The answer is Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. This combination is correct because Azure Data Factory provides the orchestration and scheduling layer to trigger the pipeline on a schedule and handle failures with built-in retry policies, while Azure Databricks executes the Spark-based cleaning and transformation of the CSV files, and Azure Synapse Analytics serves as the target data warehouse for large-scale reporting. On the Microsoft Azure Data Fundamentals DP-900 exam, this scenario tests your understanding of how Azure Data Factory orchestrates ETL workflows by integrating with compute services like Databricks and storage services like Synapse Analytics. A common trap is choosing only Databricks and Synapse, forgetting that Data Factory is essential for automated scheduling and failure handling. Remember the memory tip: “Data Factory drives, Databricks transforms, Synapse stores.”
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 retail company ingests daily sales data from multiple stores as CSV files stored in Azure Blob Storage. The data must be cleaned and transformed using Spark, then loaded into Azure Synapse Analytics for large-scale reporting. The pipeline must run on a schedule, handle failures with retries, and minimize manual intervention. Which combination of Azure services should they use to orchestrate and execute this pipeline?
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.
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 Data Factory, Azure Databricks, and Azure Synapse Analytics.
Option A is correct because Azure Data Factory provides the orchestration and scheduling layer, Azure Databricks executes the Spark-based cleaning and transformation, and Azure Synapse Analytics serves as the target data warehouse for large-scale reporting. This combination supports retry policies for failure handling and minimizes manual intervention through automated pipeline execution.
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 Data Factory, Azure Databricks, and Azure Synapse Analytics.
Why this is correct
Correct. ADF orchestrates the pipeline, Databricks performs Spark-based transformations, and Synapse Analytics serves as the data warehouse for reporting.
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.
- ✗
Azure Stream Analytics, Azure Data Lake Storage, and Power BI.
Why it's wrong here
Stream Analytics is for real-time stream processing, not scheduled batch processing of CSV files. Power BI is a visualization tool, not a transformation or storage engine.
- ✗
Azure Functions, Azure SQL Database, and Azure Analysis Services.
Why it's wrong here
Azure Functions is suitable for small-scale event-driven processing, not complex Spark transformations. Azure SQL Database is not designed for large-scale data warehousing workloads, and Analysis Services is a semantic model layer, not a data warehouse.
- ✗
Azure Logic Apps, Azure HDInsight, and Azure Cosmos DB.
Why it's wrong here
Logic Apps can orchestrate but lacks deep integration with Spark. Azure HDInsight is a managed Hadoop/Spark service but is less integrated than Databricks with ADF. Cosmos DB is a NoSQL database, not suitable for large-scale analytical queries like those run in Synapse.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Databricks with HDInsight or overlook the need for a dedicated orchestration service like Data Factory, assuming that a compute service alone can handle scheduling and retries.
Detailed technical explanation
How to think about this question
Azure Data Factory uses linked services to connect to Blob Storage as a source and Databricks as a compute target, executing Spark notebooks via Databricks activities. The pipeline can be scheduled with triggers and configured with retry policies (e.g., exponential backoff) to handle transient failures. Azure Synapse Analytics uses PolyBase or COPY INTO for high-throughput ingestion from Databricks, enabling petabyte-scale reporting with columnar storage and MPP architecture.
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 Data Factory, Azure Databricks, and Azure Synapse Analytics. — Option A is correct because Azure Data Factory provides the orchestration and scheduling layer, Azure Databricks executes the Spark-based cleaning and transformation, and Azure Synapse Analytics serves as the target data warehouse for large-scale reporting. This combination supports retry policies for failure handling and minimizes manual intervention through automated pipeline execution.
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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Last reviewed: Jun 11, 2026
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