Exam DP-201: Designing an Azure Data Solution Practice Test
Exam DP-201: Designing an Azure Data Solution focuses on planning data platforms that support analytical workloads. The available practice questions address choices involving lake processing, reporting models, data quality, access controls, resilience, and repeatable pipeline behavior. These scenarios call for connecting business needs—such as historical analysis or reliable retries—to appropriate data architecture and implementation decisions.
Exam overview
About the Exam DP-201: Designing an Azure Data Solution Exam
Design work in this area includes choosing processing approaches for lake data, shaping reporting layers with dimensional structures, and selecting stable keys when descriptive attributes can change. Data lifecycle decisions include preserving changing attributes for historical analysis, validating records before curated storage, and tracking transformations so outputs can be tied to a processing run. Architecture scenarios also test how to reduce single-zone failure risk where a service supports zone redundancy, separate consumer access from write permissions, and keep development, test, and production configurations aligned. Pipeline design requires considering retry behavior and idempotency so repeated inputs do not create duplicate business records. Together, these concepts reflect the trade-offs involved in making analytical data dependable, traceable, and suitable for downstream reporting.
Sample Practice Questions
Review a selection from this exam's published US Practice Tests practice bank.
A data engineering team wants to run Apache Spark jobs on demand over lake data, with managed cluster infrastructure and integration with Azure data services. Which service is the best fit?
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Correct answer: AAzure Databricks is a managed analytics platform based on Apache Spark and is commonly used to process and transform large-scale data in Azure data solutions.
An analytics platform needs a dimensional model for reporting sales by product, store, and date. Which structure is generally the best starting point for the reporting layer?
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Correct answer: AA star schema organizes measurable events in a fact table and descriptive attributes in dimension tables. This structure is widely used for analytical queries and makes common reporting relationships clear.
A data service needs to remain available if a single Azure availability zone becomes unavailable. Which architecture choice directly addresses that failure scenario, when supported by the service?
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Correct answer: AZone redundancy distributes service resources across separate availability zones in a region, reducing dependence on one zone. Larger single instances and same-zone backups do not address that zone-level failure.
An analytical workload needs to join datasets by a stable identifier even though descriptive attributes such as names may change. Which key design is generally most appropriate for a dimensional model?
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Correct answer: AA surrogate key provides a stable warehouse-controlled identifier that is independent of mutable descriptive attributes. The source-system identifier can still be retained for lineage and matching.
A team wants to preserve an auditable record of data transformations and be able to reproduce an output from a particular processing run. Which practice is most helpful?
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Correct answer: AVersioned code plus recorded parameters and input references makes runs traceable and supports reproducibility. Logs and run metadata are useful evidence for troubleshooting and audit needs.
A data model must represent customers and their changing addresses while preserving the history of prior addresses for analysis. Which modeling approach best meets this need?
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Correct answer: BA historical dimension pattern, often implemented with effective start and end dates, retains prior versions and makes it possible to analyze records as they were at a point in time. Overwriting loses that history.
A data platform has separate development, test, and production environments. Which practice best reduces the risk that a manual configuration difference causes a production failure?
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Correct answer: AVersion-controlled deployment definitions make configuration changes reviewable and repeatable across environments. Environment-specific values can be supplied securely without embedding secrets in code.
How to use this practice test
The free US Practice Tests practice session presents up to 20 randomly selected questions from the 50-question bank. Use each scenario to compare design choices, then review the results shown after the session to identify concepts to revisit, such as dimensional modeling, validation, access separation, or idempotent processing.
Preparation tips
- Sketch a dimensional reporting model for a sales scenario, identifying the fact table, dimensions, and stable keys used to connect records.
- Work through a changing-address example and distinguish a model that preserves prior values from one that overwrites them.
- For pipeline scenarios, trace what happens when a run is retried; check whether writes are idempotent and how malformed records are handled before curated storage.
- Compare access patterns for analytical consumers and data-preparation processes, and identify where read-only permissions should be enforced.
- Map development, test, and production settings to a repeatable deployment approach, and distinguish zone-failure mitigation from other availability measures.