DP-900 Describe core data concepts Practice Question
Which TWO Azure services are primarily used for batch processing of large volumes of data? (Choose two.)
⚠ Common exam trap
Many candidates confuse Azure Data Lake Storage (a storage service) with a processing service, or mistakenly think Azure SQL Database can handle large-scale batch processing due to its ability to run bulk insert operations, but it lacks the distributed compute and parallel architecture required for true batch processing at scale.
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 Synapse Analytics
Azure Synapse Analytics (A) is correct because it is a cloud-based analytics service that combines big data (Spark) and data warehousing (SQL pools) to process and analyze massive volumes of data in batch workloads. Azure Databricks (D) is correct because it is an Apache Spark-based analytics platform designed for large-scale batch data processing, machine learning, and ETL jobs. Azure SQL Database (B) is a transactional relational database service optimized for OLTP workloads, not large-scale batch processing. Azure Stream Analytics (C) is a real-time streaming analytics service for continuous event data, not batch processing. Azure Data Lake Storage (E) is a scalable storage layer for big data, but it stores data rather than performing batch processing itself.
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 Synapse Analytics
Why this is correct
Azure Synapse Analytics provides dedicated SQL pools and Spark engines that process massive datasets in parallel, making it a primary batch-processing service. It satisfies the stem's requirement for handling large data volumes in scheduled, non-streaming workloads rather than real-time ingestion.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational OLTP engine optimised for transactional queries against modest row counts, not distributed batch processing of large volumes. It is tempting because it stores and queries data at scale, and would be correct for transactional application workloads requiring ACID guarantees rather than bulk analytical batch jobs.
- ✗
Azure Stream Analytics
Why it's wrong here
Azure Stream Analytics performs continuous real-time stream processing over unbounded event data, not batch processing of stored large volumes. It is tempting because it handles large data volumes, and would be correct for real-time ingestion, windowed aggregation, and immediate alerting scenarios rather than scheduled batch analytics.
- ✓
Azure Databricks
Why this is correct
Azure Databricks satisfies the batch-processing requirement through its Apache Spark engine, which distributes large datasets across clusters for parallel transformation. It handles high-volume workloads that exceed single-node capacity, unlike stream-only services such as Azure Stream Analytics, making it a primary choice for scheduled, large-scale batch jobs.
- ✗
Azure Data Lake Storage
Why it's wrong here
Data Lake Storage stores files for analytics; it does not execute batch jobs. The stem asks for processing services, so a storage account cannot run transformations. It is tempting because Data Lake Storage commonly holds the input data that batch engines such as Azure Databricks or Synapse Spark then read and process.
Go deeper
Related to this question
Learn chapter
Key-Value Stores and In-Memory Caching
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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