Quick Answer
The answer is Azure Databricks and Azure HDInsight. These two services are designed for big data batch processing because they both leverage distributed computing frameworks—Apache Spark in Databricks and Hadoop/Spark in HDInsight—to process massive datasets in parallel across clusters, enabling efficient extract, transform, and load (ETL) workflows and large-scale analytics. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your ability to distinguish batch processing from real-time streaming services; a common trap is confusing Azure Stream Analytics or Azure Data Lake Storage, which are for streaming or storage, not compute. Remember that batch processing means “jobs that run on a schedule or on demand to process data at rest,” and both Databricks and HDInsight are purpose-built compute engines for that task. A helpful memory tip is to think of “BATCH” as “Big Analytics Tools for Cluster Handling”—Databricks and HDInsight fit that description perfectly.
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 are designed for big data batch processing?
Azure Databricks
Batch and streaming using Spark.
Azure Data Explorer
Why wrong: Interactive analytics on time-series data.
Azure Stream Analytics
Why wrong: Real-time stream processing.
Azure Analysis Services
Why wrong: OLAP modeling.
Azure HDInsight
Batch processing using Hadoop/Spark.
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 an Apache Spark-based analytics platform optimized for batch processing large datasets, enabling ETL, data transformation, and machine learning at scale. It uses distributed computing to process data in parallel across clusters, making it ideal for big data batch workloads.
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 Databricks
Why this is correct
Batch and streaming using Spark.
Related concept
Read the scenario before looking for a memorised answer.
Azure Data Explorer
Why it's wrong here
Interactive analytics on time-series data.
Azure Stream Analytics
Why it's wrong here
Real-time stream processing.
Azure Analysis Services
Why it's wrong here
OLAP modeling.
Azure HDInsight
Why this is correct
Batch processing using Hadoop/Spark.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse real-time analytics services (like Stream Analytics or Data Explorer) with batch processing services, or mistakenly think Analysis Services handles raw big data processing when it is actually a presentation layer for pre-aggregated data.
Detailed technical explanation
Azure Databricks leverages Apache Spark's resilient distributed datasets (RDDs) and DataFrames to perform in-memory batch processing, which significantly reduces I/O overhead compared to disk-based MapReduce. In a real-world scenario, a retail company might use Databricks to run nightly batch jobs that aggregate terabytes of sales data across regions, applying transformations and writing results to a data warehouse for reporting.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 an Apache Spark-based analytics platform optimized for batch processing large datasets, enabling ETL, data transformation, and machine learning at scale. It uses distributed computing to process data in parallel across clusters, making it ideal for big data batch workloads.
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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Same concept, more angles
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Variation 1. Which TWO Azure services are primarily used for batch processing of large data volumes?
easyWhy A: Azure Databricks is correct because it provides an Apache Spark-based analytics platform that can process large volumes of data in batch mode using distributed computing. It allows you to run ETL jobs, transformations, and machine learning pipelines on data stored in Azure Data Lake Storage or other sources, making it ideal for batch processing.
Last reviewed: Jun 24, 2026
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.
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