The answer is Azure Data Factory. This is the correct choice because Azure Data Factory is a cloud-based orchestration service for modern data warehouse pipelines, designed to schedule, monitor, and manage end-to-end data workflows at scale. It natively supports triggers for automation and can orchestrate Apache Spark transformations through Azure Databricks, then load the transformed data into Azure Synapse Analytics using built-in copy activities. On the Microsoft Azure Data Fundamentals DP-900 exam, this question tests your understanding of which service provides unified pipeline management rather than just compute or storage—a common trap is confusing Azure Synapse Analytics itself with orchestration, but Synapse is the query engine, not the orchestrator. Remember the memory tip: “ADF is the conductor; Synapse is the orchestra.”
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 engineering team is designing a modern data warehouse on Azure. They have raw data landing in Azure Data Lake Storage Gen2 (ADLS Gen2) as Parquet files. They need to perform transformations using Apache Spark, and then load the transformed data into Azure Synapse Analytics for high-performance analytical queries. The team wants to use a single orchestration service to schedule, monitor, and manage the entire pipeline. Which Azure service should they choose for orchestration?
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 orchestration service designed to schedule, monitor, and manage data pipelines at scale. It natively supports triggers (e.g., time-based, event-based) and can orchestrate Apache Spark transformations via Azure Databricks or HDInsight, then load the transformed data into Azure Synapse Analytics using built-in copy activities or pipelines. ADF provides a single pane of glass for end-to-end pipeline management, including dependency handling and error monitoring.
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
Data Factory orchestrates data movement and transformation, supporting both Spark and Synapse workloads.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Databricks
Why it's wrong here
Databricks is a compute platform for Spark; it lacks native scheduling and monitoring for the full pipeline.
✗
Azure Logic Apps
Why it's wrong here
Logic Apps handles simple integrations, not complex ETL orchestration with Spark and Synapse.
✗
Azure Data Lake Analytics
Why it's wrong here
Data Lake Analytics is deprecated and not suitable for orchestrating modern pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Azure Databricks (a compute/transform service) with an orchestration tool, but the question explicitly asks for a service to 'schedule, monitor, and manage the entire pipeline,' which is the core function of Azure Data Factory, not Databricks.
Detailed technical explanation
How to think about this question
Azure Data Factory uses a JSON-based pipeline definition and supports triggers such as 'Tumbling Window' for scheduled runs and 'Event' triggers for file arrival in ADLS Gen2. Under the hood, ADF leverages Azure Integration Runtime for data movement and can invoke Azure Databricks notebooks via the 'Databricks Notebook' activity, passing parameters for dynamic transformation. In a real-world scenario, a team might use ADF to trigger a Databricks job when new Parquet files land, then use a 'Copy Data' activity to load the output into a Synapse dedicated SQL pool with PolyBase for high-throughput ingestion.
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 orchestration service designed to schedule, monitor, and manage data pipelines at scale. It natively supports triggers (e.g., time-based, event-based) and can orchestrate Apache Spark transformations via Azure Databricks or HDInsight, then load the transformed data into Azure Synapse Analytics using built-in copy activities or pipelines. ADF provides a single pane of glass for end-to-end pipeline management, including dependency handling and error monitoring.
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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