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 Explorer

  Why this is correct

  Azure Data Explorer is the correct choice because it is purpose-built for real-time analytics on high-velocity streaming data such as call detail records. It natively ingests from sources like Event Hubs and stores raw data in a columnar format for sub-second Kusto Query Language (KQL) queries. This enables interactive exploration and time-series analysis on millions of records per second, which directly fits the telco scenario of analyzing call streams as they arrive.

• ✗

  Azure Synapse Analytics

  Why it's wrong here

  Azure Synapse Analytics is a unified big data and data warehousing platform, but it is optimized for large-scale batch processing and T-SQL queries over structured and semi-structured data. While it can integrate Spark and pipelines, it does not natively ingest streaming data for immediate interactive querying, and it does not use the Kusto Query Language. For live call detail analysis requiring low-latency exploration, Synapse would add unnecessary overhead and fail to deliver the real-time performance needed.

  When this WOULD be correct

  A company needs to run complex T-SQL queries across petabytes of structured and unstructured data in a data warehouse, with integrated data pipelines and BI integration.

• ✗

  Azure Stream Analytics

  Why it's wrong here

  Azure Stream Analytics is a real-time stream processing engine that executes continuous queries on live data and outputs results, but it does not persist the raw incoming data for ad hoc interactive queries. It is typically used to transform, aggregate, or route streaming events to a sink such as Event Hubs, Azure Data Lake, or SQL Database, meaning you would need a separate analytical store to query the call details. Therefore, while it can process the stream, it is not the right tool for the direct, KQL-based exploratory analysis described in the question.

  When this WOULD be correct

  A company needs to process a continuous stream of IoT sensor data, apply real-time transformations (e.g., aggregations, filtering), and output results to a dashboard or storage without needing to query historical data interactively. The primary requirement is low-latency stream processing, not ad-hoc analytics with KQL.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is a semantic modeling and Business Intelligence layer that aggregates metrics from existing data sources like Azure SQL Data Warehouse or SQL databases. It is not a streaming ingestion engine—it relies on pre-loaded or processed data and cannot natively receive or store raw, unaggregated call detail records at high velocity. Using it for real-time CDR exploration would require an upstream store to first capture the data, making it unsuitable for the described scenario.

  When this WOULD be correct

  A company needs to create a semantic data model for business intelligence reporting over historical sales data from a data warehouse, with fast query performance for Excel and Power BI users.

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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