DP-900 Describe an analytics workload on Azure Practice Question
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?
⚠ Common exam trap
Watch out — candidates often 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.
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.
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.
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
Azure Data Factory (ADF) orchestrates the end-to-end pipeline, executing scheduled triggers to copy daily CSV files from store locations into Azure Data Lake Storage (ADLS). Azure Databricks then attaches to that data and runs Apache Spark jobs for scalable transformations—such as cleaning, deduplication, and aggregate sales metrics—that are hard to express in T-SQL. Finally, Azure Synapse Analytics loads the transformed data into a dedicated SQL pool or exposes it via serverless SQL, acting as the central data warehouse that supports fast, concurrent reporting queries. This trio forms a cohesive modern data warehouse pattern: ADF for control flow, Databricks for complex compute, and Synapse for the serving layer.
- ✗
Azure Stream Analytics, Azure Data Lake Storage, and Power BI.
Why it's wrong here
Azure Stream Analytics is a real-time, event-stream processing engine built for data in motion (e.g., IoT telemetry or live clickstreams), not for scheduled batch orchestration of static CSV files—it lacks the pipeline control flow needed to move files and trigger dependent activities. Azure Data Lake Storage (ADLS) is purely a scalable file store; it provides no compute engine to transform or model data, so it cannot replace a transformation service like Databricks or a warehouse engine. Power BI is a reporting and visualization tool that consumes prepared data, not a storage or processing layer, and it cannot perform the heavy ETL/ELT required by daily store sales. This set omits both an orchestration service (like ADF) and a dedicated analytic warehouse (like Synapse), making it fundamentally ill-suited for a batch analytics workflow.
When this WOULD be correct
A company needs to analyze real-time IoT sensor data from devices, transform it with windowed aggregations, and visualize live dashboards. The correct answer would be Azure Stream Analytics for processing, Azure Data Lake Storage for landing data, and Power BI for dashboards.
- ✗
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.
When this WOULD be correct
A company needs to process real-time streaming data (e.g., IoT sensor readings) with simple transformations, store results in a relational database for transactional queries, and provide a semantic model for reporting. In that case, Azure Functions (for lightweight processing), Azure SQL Database (for storage), and Azure Analysis Services (for modeling) would be appropriate.
- ✗
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.
When this WOULD be correct
A company needs to process real-time IoT sensor data using Spark Streaming on HDInsight, store results in Cosmos DB for low-latency access, and orchestrate the pipeline with Logic Apps triggered by event-based schedules. This scenario requires event-driven, serverless orchestration for streaming data.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.Correct answer▾
Why this is correct
Azure Data Factory (ADF) orchestrates the end-to-end pipeline, executing scheduled triggers to copy daily CSV files from store locations into Azure Data Lake Storage (ADLS). Azure Databricks then attaches to that data and runs Apache Spark jobs for scalable transformations—such as cleaning, deduplication, and aggregate sales metrics—that are hard to express in T-SQL. Finally, Azure Synapse Analytics loads the transformed data into a dedicated SQL pool or exposes it via serverless SQL, acting as the central data warehouse that supports fast, concurrent reporting queries. This trio forms a cohesive modern data warehouse pattern: ADF for control flow, Databricks for complex compute, and Synapse for the serving layer.
✗Azure Stream Analytics, Azure Data Lake Storage, and Power BI.Wrong answer — click to see why▾
Why this is wrong here
Azure Stream Analytics is for real-time streaming, not batch CSV ingestion; Power BI is a visualization tool, not an orchestration or transformation service. The pipeline requires scheduled batch processing with Spark, which Stream Analytics does not support.
★ When this WOULD be the correct answer
A company needs to analyze real-time IoT sensor data from devices, transform it with windowed aggregations, and visualize live dashboards. The correct answer would be Azure Stream Analytics for processing, Azure Data Lake Storage for landing data, and Power BI for dashboards.
Why candidates choose this
Candidates may associate Azure Data Lake Storage with data lakes and Power BI with reporting, overlooking that the question specifies batch CSV ingestion and Spark transformations, which Stream Analytics cannot handle.
✗Azure Functions, Azure SQL Database, and Azure Analysis Services.Wrong answer — click to see why▾
Why this is wrong here
Azure Functions is event-driven and not designed for orchestrated, scheduled ETL pipelines with retry logic; Azure SQL Database lacks the large-scale parallel processing needed for big data transformations, and Azure Analysis Services is for semantic modeling, not data ingestion or transformation.
★ When this WOULD be the correct answer
A company needs to process real-time streaming data (e.g., IoT sensor readings) with simple transformations, store results in a relational database for transactional queries, and provide a semantic model for reporting. In that case, Azure Functions (for lightweight processing), Azure SQL Database (for storage), and Azure Analysis Services (for modeling) would be appropriate.
Why candidates choose this
Candidates may recognize Azure Functions as a serverless compute option and Azure SQL Database as a common data store, but they overlook the need for a dedicated orchestration service (like Data Factory) and a big data processing engine (like Spark) for scheduled, resilient ETL on large CSV files.
✗Azure Logic Apps, Azure HDInsight, and Azure Cosmos DB.Wrong answer — click to see why▾
Why this is wrong here
Azure Logic Apps is not designed for big data orchestration with Spark, and Azure Cosmos DB is a NoSQL database not suited for large-scale reporting workloads like Azure Synapse Analytics. HDInsight could run Spark, but the combination lacks a unified orchestration service like Data Factory for scheduling and retries.
★ When this WOULD be the correct answer
A company needs to process real-time IoT sensor data using Spark Streaming on HDInsight, store results in Cosmos DB for low-latency access, and orchestrate the pipeline with Logic Apps triggered by event-based schedules. This scenario requires event-driven, serverless orchestration for streaming data.
Why candidates choose this
Candidates may think HDInsight can replace Databricks for Spark processing and that Logic Apps can orchestrate scheduled pipelines, overlooking that Data Factory is the proper service for batch orchestration with retry and monitoring capabilities.
Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
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