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.

• ✓

  Ability to query data in the data lake using serverless SQL

  Why this is correct

  The serverless SQL pool in Azure Synapse allows you to query data directly in Azure Data Lake Storage (ADLS) using standard T-SQL syntax, without the need to load or copy the data into a separate storage system. This on-demand, pay-per-query capability enables ad-hoc exploration, transformation, and analysis of raw files (e.g., Parquet, CSV) at scale, making it a cost-effective and low-latency way to gain insights from your data lake.

• ✗

  Native support for MongoDB data sources

  Why it's wrong here

  Azure Synapse Analytics does not provide native support for MongoDB as a data source. While Synapse integrates natively with many Azure services (like Blob Storage, Azure Data Lake, Cosmos DB, and SQL Server), MongoDB requires custom code, third-party connectors, or external ETL tools to bring its data into the pipeline. Therefore, MongoDB support is not a benefit of using Synapse, as it adds integration complexity rather than simplifying it.

• ✓

  Unified experience for data integration, warehousing, and big data analytics

  Why this is correct

  Synapse provides a unified analytics platform that brings together data integration (via Synapse Pipelines, based on Azure Data Factory), data warehousing (through dedicated and serverless SQL pools), and big data analytics (with built-in Apache Spark pools). This consolidation enables teams to ingest, prepare, manage, and serve data for business intelligence and machine learning workflows within a single workspace, eliminating the need to switch between multiple tools and reducing operational overhead.

• ✗

  Automatic indexing of all data

  Why it's wrong here

  Azure Synapse does not automatically index all data across the platform. While dedicated SQL pools support indexes (such as clustered and nonclustered indexes) on tables, you must define and manage these manually, and automatic indexing is not applied to raw files in the data lake or to serverless SQL queries. The idea that Synapse automatically indexes every piece of data is a common misconception, as indexing is a table-level feature that requires intentional configuration.

• ✗

  Built-in email alerts for query performance

  Why it's wrong here

  Synapse does not have built-in email alerts specifically for query performance. Although you can configure alerts using Azure Monitor (e.g., for serverless SQL pool metrics), these are separate Azure services and not an intrinsic feature of Synapse Analytics itself. Additionally, email alerts are a generic monitoring capability, not a differentiator or benefit of using Synapse, and setting them up requires extra configuration outside the native environment.

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