DP-900 Describe an analytics workload on Azure Practice Question

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

  Why it's wrong here

  Azure Data Factory is an ETL and orchestration service. It does not provide a direct SQL query interface over data in a data lake; it moves or transforms data but does not serve interactive queries.

  When this WOULD be correct

  A company needs to ingest data from multiple sources into Azure Data Lake Storage Gen2 on a scheduled basis, performing transformations like filtering and aggregation before loading. The question would emphasize orchestration and data movement, not direct querying.

• ✓

  Azure Synapse Serverless SQL pool

  Why this is correct

  Serverless SQL pool (part of Azure Synapse Analytics) enables querying data directly from Azure Data Lake Storage using standard T-SQL. It is serverless (no infrastructure to manage) and perfect for ad-hoc analytics integration with Power BI.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks is an Apache Spark-based analytics platform that runs on clusters you provision and manage. While it can read Parquet files in Azure Data Lake Storage Gen2 using Spark SQL or DataFrames, it does not expose a standard T-SQL endpoint and typically incurs cluster startup latency and operational overhead. Even when using SQL endpoints, the experience is Spark-centric, not a serverless, on-demand T-SQL query service like Azure Synapse Serverless SQL pool. Thus it fits batch/ETL and machine learning workloads rather than simple ad-hoc T-SQL queries for Power BI.

  When this WOULD be correct

  A company needs to perform advanced analytics, including machine learning and real-time data processing, on large datasets stored in Azure Data Lake Storage Gen2. They require a collaborative environment for data scientists and engineers to run custom code in Python, Scala, or SQL, and they are willing to manage cluster resources. In this scenario, Azure Databricks would be the correct answer.

• ✗

  Azure HDInsight

  Why it's wrong here

  Azure HDInsight requires provisioning and managing a cluster (e.g., Spark or Hive) to process data, which introduces startup latency and operational overhead, contradicting the requirement to avoid moving data and minimise duplication. It is tempting because HDInsight can query data in Data Lake Storage Gen2 using Hive or Spark SQL, making it a plausible choice for complex SQL workloads in a big-data context, and would be correct if the company needed custom cluster configurations or batch-processing pipelines beyond simple serverless querying.

  When this WOULD be correct

  A company needs to run custom MapReduce or Spark jobs on large datasets stored in Azure Data Lake Storage Gen2, and they require full control over cluster configuration, including specific versions of Hadoop, Spark, or Hive. They are willing to manage cluster lifecycle and pay for compute resources.

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.

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?”

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