Quick Answer
The correct answers are Azure Databricks and Azure Synapse Analytics, as both provide fully managed Apache Spark environments designed for interactive ad-hoc analytics on large datasets. Azure Databricks excels with its collaborative notebook interface and auto-scaling clusters optimized for in-memory processing, enabling data engineers to run iterative queries and machine learning workloads in real time. Azure Synapse Analytics offers a unified platform that integrates Spark pools with serverless SQL and dedicated SQL pools, allowing you to perform ad-hoc analysis across data lakes and warehouses without provisioning infrastructure. On the DP-900 exam, this question tests your understanding of which Azure services natively support Spark for interactive workloads—a common trap is confusing Azure HDInsight (which also runs Spark but is more complex to manage) with these simpler, PaaS-based options. A helpful memory tip: think of Databricks as the “notebook-first” Spark service for data science, and Synapse as the “all-in-one” analytics hub that combines Spark with SQL.
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.
Which TWO Azure services can be used to perform interactive ad-hoc analytics on large datasets using Apache Spark?
Azure Analysis Services
Why wrong: Provides OLAP models, not Spark.
Azure HDInsight
Why wrong: Can run Spark but is less interactive and ad-hoc focused.
Azure Databricks
Fully managed Spark platform for analytics.
Azure Synapse Analytics
Has built-in Apache Spark pools for interactive analytics.
Azure Data Lake Storage
Why wrong: Storage service, not compute.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Azure Databricks
Azure Databricks is correct because it provides a fully managed Apache Spark platform optimized for interactive ad-hoc analytics, allowing data engineers and data scientists to run Spark jobs in collaborative notebooks with auto-scaling clusters. It supports interactive queries, real-time dashboards, and machine learning workloads on large datasets using Spark's in-memory processing engine.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Azure Analysis Services
Why it's wrong here
Provides OLAP models, not Spark.
Azure HDInsight
Why it's wrong here
Can run Spark but is less interactive and ad-hoc focused.
Azure Databricks
Why this is correct
Fully managed Spark platform for analytics.
Related concept
Read the scenario before looking for a memorised answer.
Azure Synapse Analytics
Why this is correct
Has built-in Apache Spark pools for interactive analytics.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Lake Storage
Why it's wrong here
Storage service, not compute.
Common exam traps
The trap here is that candidates often confuse Azure HDInsight (which does support Spark) with a service optimized for interactive ad-hoc analytics, but HDInsight is more suited for batch and scheduled workloads, not the real-time, collaborative, and auto-scaling environment that Databricks provides.
Detailed technical explanation
Under the hood, Azure Databricks uses a managed Spark cluster with optimized I/O and caching (e.g., Delta Lake for ACID transactions) to accelerate interactive queries, while Azure Synapse Analytics integrates Apache Spark pools with its SQL engine for unified big data and data warehousing. In a real-world scenario, a data analyst might use Databricks notebooks to run ad-hoc SQL queries on terabytes of streaming data, leveraging Spark's Catalyst optimizer for query planning and Tungsten execution engine for efficient memory management.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Azure Databricks — Azure Databricks is correct because it provides a fully managed Apache Spark platform optimized for interactive ad-hoc analytics, allowing data engineers and data scientists to run Spark jobs in collaborative notebooks with auto-scaling clusters. It supports interactive queries, real-time dashboards, and machine learning workloads on large datasets using Spark's in-memory processing engine.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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