The correct answers are Azure Synapse Analytics and Azure Databricks, as these two services are the primary Azure batch processing services for handling large volumes of data. Azure Synapse Analytics uses massively parallel processing (MPP) to run complex queries and bulk transformations, while Azure Databricks leverages Apache Spark’s distributed computing to execute batch ETL jobs across clusters using DataFrames and RDDs. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your ability to distinguish batch processing from real-time streaming—a common trap is confusing Azure Stream Analytics (a streaming service) with these batch-oriented tools. A helpful memory tip is to think of “batch” as “big and bulky,” which matches Synapse’s MPP engine and Databricks’ Spark clusters, both designed to process data in large, scheduled chunks rather than continuous streams.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. 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 are primarily used for batch processing of large volumes of data? (Choose two.)
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 is correct because it provides a cloud-based data warehousing and analytics service that uses massively parallel processing (MPP) to run complex queries and batch processing jobs over large datasets, often using PolyBase or T-SQL to transform and load data in bulk. Azure Databricks is correct because it is an Apache Spark-based analytics platform optimized for batch processing, allowing users to run distributed data processing jobs (e.g., ETL, data transformation) across large volumes of data using DataFrames and RDDs in a cluster environment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Synapse provides SQL and Spark engines for batch processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure SQL Database
Why it's wrong here
SQL Database is for transactional workloads, not batch processing.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is for real-time stream processing.
✓
Azure Databricks
Why this is correct
Databricks provides Apache Spark for batch and stream processing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Lake Storage
Why it's wrong here
Data Lake Storage is a storage layer, not a processing service.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, Azure Synapse Analytics uses a control node to distribute queries across compute nodes via the MPP engine, leveraging columnar storage (SQL Server columnstore indexes) for high compression and scan performance during batch loads. Azure Databricks runs Apache Spark jobs on managed clusters, where data is processed in-memory across partitions using the Catalyst optimizer for query planning, and it supports Delta Lake for ACID transactions on batch data. A real-world scenario: a retail company uses Synapse to run nightly batch aggregations on terabytes of sales data, while Databricks handles complex ETL pipelines that transform raw logs into structured tables for downstream analytics.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Synapse Analytics — Azure Synapse Analytics is correct because it provides a cloud-based data warehousing and analytics service that uses massively parallel processing (MPP) to run complex queries and batch processing jobs over large datasets, often using PolyBase or T-SQL to transform and load data in bulk. Azure Databricks is correct because it is an Apache Spark-based analytics platform optimized for batch processing, allowing users to run distributed data processing jobs (e.g., ETL, data transformation) across large volumes of data using DataFrames and RDDs in a cluster environment.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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