DP-900 Describe an analytics workload on Azure Practice Question
A data engineering team is building a data pipeline to run daily batch loads from an on-premises SQL Server to Azure Synapse Analytics. The pipeline must include data transformation using a visual interface with no coding, and must support schema mapping and data validation. Which THREE Azure services should be used together?
⚠ Common exam trap
Many exam-takers assume Azure Databricks is required for transformations, but the question explicitly requires a visual interface with no coding, which Azure Data Factory's Mapping Data Flows provide, not Databricks' notebook-based approach.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Synapse Analytics
Azure Synapse Analytics is the correct destination for the pipeline because it is a cloud-based data warehouse that supports high-performance analytics on large-scale data, making it ideal for daily batch loads from SQL Server. It integrates natively with Azure Data Factory for orchestration and Azure Blob Storage for staging, enabling schema mapping and data validation through visual interfaces without coding.
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 Synapse Analytics
Why this is correct
Azure Synapse Analytics is the correct target serving layer because it unifies data warehousing, serverless SQL, Apache Spark, and Pipelines in a single workspace. After the pipeline transforms and stages data, Synapse SQL pools or serverless endpoints provide high-performance T-SQL queries over relational and data lake data. This makes Synapse the actual queryable destination where analytics consumers connect, fulfilling the pipeline's serving requirement.
- ✓
Azure Data Factory
Why this is correct
Azure Data Factory is the right choice for visual ETL and orchestration because it provides a cloud ELT service with an interactive canvas, connectors to 100+ sources, and scheduling/triggering capabilities. Data engineers can build pipelines using copy activities and mapping data flows without writing custom integration code, while still being able to invoke compute like Databricks or Synapse Spark. Its visual control-flow design directly matches the need for a code-free pipeline building interface.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is wrong because, although it is a powerful Apache Spark analytics platform, it is primarily a code-first environment using notebooks in Python, Scala, or SQL. Building data transformations in Databricks requires writing explicit Spark code and managing cluster configuration, unlike a visual drag-and-drop interface. While it can certainly process data in a pipeline, its development model depends on a data engineering team's coding effort, so it fails the 'visual interface' criterion in the scenario.
- ✓
Azure Blob Storage
Why this is correct
Azure Blob Storage is a correct component because it is massively scalable object storage used as the landing zone and staging area in data pipelines. Raw files, intermediate CSV/Parquet/JSON outputs, and even final data lake tables can reside in Blob containers at low cost before being loaded into a serving layer. Its role is to hold data durably during pipeline execution and support file-based operations, not to provide SQL-based analytics or orchestration itself, which is why it is complementary to the other services.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is wrong for this pipeline requirement because it is a semantic modeling engine that consumes already-loaded and cleaned data, not a pipeline processing or serving layer. It builds tabular or multidimensional models with in-memory compression, measures, and KPIs for business intelligence tools like Power BI or Excel. Since Analysis Services operates after a data warehouse or data lake is populated, selecting it for the pipeline's main serving role would imply using it prematurely for data that is not yet modeled or prepared.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Blob storage
Blob storage is a cloud service for storing large amounts of unstructured data, such as text or binary data, like documents, images, and videos.
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