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 company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every night, they need to load 500 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2. The loading process must be automated, scheduled, and include error handling (e.g., skip corrupt rows and log them). Which Azure service should be used to orchestrate this load pipeline?
Correct. Azure Data Factory can orchestrate the copy activity on a schedule, handle errors with custom logic, and scale to move 500 GB daily.
B
Azure Stream Analytics
Why wrong: Stream Analytics is for real-time stream processing, not for scheduled batch loading of large files.
C
Azure HDInsight
Why wrong: HDInsight is a big data cluster (e.g., Spark, Hive) that can process data, but it requires more overhead to manage and is not primarily an orchestration service for scheduled loads.
D
Azure Logic Apps
Why wrong: Logic Apps can orchestrate workflows but is not optimized for large-scale data movement; it lacks native connectors for high-volume data transfer to Synapse.
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
Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service that supports scheduled execution, error handling (e.g., skipping corrupt rows via fault tolerance settings in the Copy activity), and native integration with Azure Data Lake Storage Gen2 and Azure Synapse dedicated SQL pool. ADF can automate the nightly 500 GB load using a trigger, and its mapping data flows or Copy activity can log errors to a separate file or table, meeting the requirement for automated, scheduled, and error-tolerant ingestion.
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 Data Factory
Why this is correct
Correct. Azure Data Factory can orchestrate the copy activity on a schedule, handle errors with custom logic, and scale to move 500 GB daily.
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, not for scheduled batch loading of large files.
✗
Azure HDInsight
Why it's wrong here
HDInsight is a big data cluster (e.g., Spark, Hive) that can process data, but it requires more overhead to manage and is not primarily an orchestration service for scheduled loads.
✗
Azure Logic Apps
Why it's wrong here
Logic Apps can orchestrate workflows but is not optimized for large-scale data movement; it lacks native connectors for high-volume data transfer to Synapse.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure Data Factory with Azure Logic Apps because both support scheduling and automation, but Logic Apps lacks the native data movement capabilities and fault tolerance for large-scale batch ETL workloads like loading 500 GB into a dedicated SQL pool.
Detailed technical explanation
How to think about this question
Under the hood, Azure Data Factory uses the Copy activity with a PolyBase or COPY INTO statement to load data into Synapse dedicated SQL pool, leveraging parallel partitioning for high throughput. The fault tolerance setting in the Copy activity allows you to skip incompatible rows (e.g., corrupt CSV records) and redirect them to a separate error file or log, which is crucial for nightly loads where data quality may vary. In a real-world scenario, if a CSV file contains malformed rows, ADF can continue processing the rest of the data and log the errors to an Azure Blob Storage sink, ensuring the pipeline completes without manual intervention.
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 — Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service that supports scheduled execution, error handling (e.g., skipping corrupt rows via fault tolerance settings in the Copy activity), and native integration with Azure Data Lake Storage Gen2 and Azure Synapse dedicated SQL pool. ADF can automate the nightly 500 GB load using a trigger, and its mapping data flows or Copy activity can log errors to a separate file or table, meeting the requirement for automated, scheduled, and error-tolerant ingestion.
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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