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.
A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?
Azure Stream Analytics with a reference data input.
Why wrong: Stream Analytics is for real-time streaming data, not scheduled batch processing of files in Blob Storage.
B
Azure Data Factory with a Mapping Data Flow.
Mapping Data Flows support schema drift and can be scheduled to run on a recurring basis, making it ideal for this scenario.
C
Azure SQL Database with a stored procedure.
Why wrong: SQL Database is not designed to directly transform JSON files from Blob Storage without additional tools like PolyBase or BULK INSERT, and schema changes require manual script updates.
D
Azure Logic Apps with a JSON parser.
Why wrong: Logic Apps are for business process automation and orchestration, not for large-scale data transformation.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Factory with a Mapping Data Flow.
Azure Data Factory with a Mapping Data Flow is correct because it provides a code-free, visual data transformation environment that can run on a scheduled trigger (every hour), handle schema drift automatically via schema drift options in Mapping Data Flows, and process JSON files from Azure Blob Storage into a structured format for reporting. This meets all requirements: scheduled execution, transformation, and schema evolution without manual intervention.
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 Stream Analytics with a reference data input.
Why it's wrong here
Stream Analytics is for real-time streaming data, not scheduled batch processing of files in Blob Storage.
✓
Azure Data Factory with a Mapping Data Flow.
Why this is correct
Mapping Data Flows support schema drift and can be scheduled to run on a recurring basis, making it ideal for this scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure SQL Database with a stored procedure.
Why it's wrong here
SQL Database is not designed to directly transform JSON files from Blob Storage without additional tools like PolyBase or BULK INSERT, and schema changes require manual script updates.
✗
Azure Logic Apps with a JSON parser.
Why it's wrong here
Logic Apps are for business process automation and orchestration, not for large-scale data transformation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Stream Analytics (real-time) with batch processing, or think Azure Logic Apps can handle complex data transformations, when in fact Data Factory is the correct service for scheduled, schema-drift-tolerant ETL on Azure.
Detailed technical explanation
How to think about this question
Mapping Data Flows in Azure Data Factory execute on Spark clusters behind the scenes, allowing for distributed, in-memory transformations that can handle schema drift by using 'drift' column mappings and 'flexible schema' settings. The hourly schedule is implemented via a tumbling window trigger, which ensures the pipeline runs exactly at the specified interval, even if the previous run is still in progress. In real-world scenarios, clickstream data often has new fields added (e.g., new tracking parameters), and schema drift in Mapping Data Flows automatically maps these new columns to the sink without breaking the pipeline.
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 Data Factory with a Mapping Data Flow. — Azure Data Factory with a Mapping Data Flow is correct because it provides a code-free, visual data transformation environment that can run on a scheduled trigger (every hour), handle schema drift automatically via schema drift options in Mapping Data Flows, and process JSON files from Azure Blob Storage into a structured format for reporting. This meets all requirements: scheduled execution, transformation, and schema evolution without manual intervention.
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.