The answer is Azure Databricks and Azure Synapse Analytics serverless SQL pool. Azure Databricks is correct because it provides a unified analytics platform with native Delta Lake support, enabling ACID transactions, schema enforcement, and time travel on data lakes—the core requirements of a lakehouse architecture. Azure Synapse Analytics serverless SQL pool is correct because it can directly query Delta Lake tables using its built-in Delta format reader, allowing for serverless T-SQL analytics over the same data without moving it. On the DP-203 exam, this question tests your understanding of which Azure services natively integrate with Delta Lake rather than just supporting generic data lakes; a common trap is selecting Azure Data Lake Storage Gen2 alone, which is only the storage layer, not the compute engine. Remember the mnemonic “DAD” for Delta Lake on Azure: Databricks for engineering, Azure Synapse serverless for SQL, and Data Lake Storage Gen2 for the underlying store.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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.
Which TWO Azure services can be used to implement a data lakehouse architecture with Delta Lake?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Databricks
Azure Databricks is correct because it provides a unified analytics platform with native Delta Lake support, enabling ACID transactions, schema enforcement, and time travel on data lakes. Delta Lake is an open-source storage layer that brings reliability to data lakes, and Azure Databricks is the primary service for running Delta Lake workloads at scale.
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 Databricks
Why this is correct
Databricks is the primary platform for Delta Lake.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Data Factory
Why it's wrong here
ADF can copy data to/from Delta Lake but does not implement lakehouse natively.
✗
Azure Cosmos DB
Why it's wrong here
Cosmos DB is a NoSQL database, not a data lake.
✓
Azure Synapse Analytics serverless SQL pool
Why this is correct
Serverless SQL pool supports querying Delta Lake tables via OPENROWSET.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure SQL Database
Why it's wrong here
Azure SQL Database does not support Delta Lake format.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Azure Data Factory can implement a data lakehouse because it can copy data to ADLS, but they miss that a data lakehouse requires a compute engine (like Spark or serverless SQL) and a transactional storage layer (Delta Lake), not just data movement.
Detailed technical explanation
How to think about this question
Delta Lake uses a transaction log (stored as JSON files in the _delta_log directory) to track all changes, enabling ACID guarantees on top of Parquet files. Azure Synapse Analytics serverless SQL pool can query Delta Lake tables via the OPENROWSET function with the FORMAT='DELTA' option, allowing T-SQL users to access the data lakehouse without moving data. This integration is critical for scenarios where data engineers use Databricks for ETL and data analysts need SQL-based access to the same Delta tables.
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-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Databricks — Azure Databricks is correct because it provides a unified analytics platform with native Delta Lake support, enabling ACID transactions, schema enforcement, and time travel on data lakes. Delta Lake is an open-source storage layer that brings reliability to data lakes, and Azure Databricks is the primary service for running Delta Lake workloads at scale.
What should I do if I get this DP-203 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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Question Discussion
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