DP-900 Describe an analytics workload on Azure Practice Question
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 primarily used for batch processing of large data volumes?
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 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.
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 Databricks
Why this is correct
Databricks is a unified analytics platform for batch and streaming.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Lake Storage
Why it's wrong here
This is a storage service, not a processing service.
✓
Azure Synapse Pipelines
Why this is correct
Synapse Pipelines orchestrate batch data movement and transformation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is for real-time stream processing.
✗
Azure Logic Apps
Why it's wrong here
Logic Apps is for workflow automation, not large-scale batch processing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse storage services (like Azure Data Lake Storage) with processing services, or they mistakenly think stream processing tools (like Stream Analytics) can handle batch workloads, when in fact batch processing requires tools designed for static, large-scale data transformations.
Detailed technical explanation
How to think about this question
Azure Synapse Pipelines (formerly Azure Data Factory) orchestrates and executes data movement and transformation at scale, supporting both code-free and code-based batch ETL/ELT workflows. Under the hood, it uses distributed compute resources (e.g., Integration Runtimes) to parallelize data processing across partitions, and it can invoke Azure Databricks or HDInsight for heavy lifting. A real-world scenario is a nightly pipeline that ingests terabytes of clickstream logs from Blob Storage, transforms them via Databricks notebooks, and loads the results into a Synapse dedicated SQL pool for reporting.
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 an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Databricks — 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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.