- A
Azure Data Lake Storage Gen2
ADLS Gen2 provides hierarchical namespace and is optimized for analytics workloads.
- B
Azure Synapse SQL Pool
Why wrong: Synapse SQL Pool is a compute service for querying, not a storage layer.
- C
Azure Blob Storage
Why wrong: Blob Storage lacks hierarchical namespace, which is needed for data lake operations.
- D
Azure SQL Database
Why wrong: Azure SQL is a relational database, not suitable for storing Parquet files at scale.
Question 794 of 851
Azure Data Lake Storage Gen2 for Analytics — Choosing the Primary Storage Layer
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.
You are designing a data lake for a manufacturing company that will store sensor readings in Parquet format. The data will be used by data scientists for batch training and by analysts for ad-hoc queries. Which Azure service should you use as the primary storage layer?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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 Data Lake Storage Gen2
Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct primary storage layer because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing POSIX-compliant access control and directory-level operations. This makes it ideal for storing Parquet files used by data scientists for batch training (via Spark or PolyBase) and by analysts for ad-hoc queries (via Synapse SQL or Azure Databricks), as it supports both high-throughput analytics and fine-grained security.
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 Lake Storage Gen2
Why this is correct
ADLS Gen2 provides hierarchical namespace and is optimized for analytics workloads.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure Synapse SQL Pool
Why it's wrong here
Synapse SQL Pool is a compute service for querying, not a storage layer.
- ✗
Azure Blob Storage
Why it's wrong here
Blob Storage lacks hierarchical namespace, which is needed for data lake operations.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL is a relational database, not suitable for storing Parquet files at scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing a compute service (Synapse SQL Pool) with a storage service, or assuming that Blob Storage alone is sufficient without considering the hierarchical namespace required for data lake operations.
Detailed technical explanation
How to think about this question
ADLS Gen2 uses a hierarchical namespace that allows renaming and deleting directories atomically, which is critical for managing Parquet partitions in batch training pipelines. Under the hood, it exposes a REST API compatible with both Blob Storage and HDFS, enabling tools like Apache Spark to read/write Parquet files with full ACID compliance via Delta Lake or Apache Hudi. In a real-world scenario, a manufacturing company might use ADLS Gen2 to store sensor data partitioned by date and sensor ID, allowing data scientists to run distributed training jobs while analysts query subsets without full table scans.
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.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-203 question test?
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 Data Lake Storage Gen2 — Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct primary storage layer because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing POSIX-compliant access control and directory-level operations. This makes it ideal for storing Parquet files used by data scientists for batch training (via Spark or PolyBase) and by analysts for ad-hoc queries (via Synapse SQL or Azure Databricks), as it supports both high-throughput analytics and fine-grained security.
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.
Are there clue words in this question I should notice?
Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are designing a data storage solution for a retail company that needs to store semi-structured IoT sensor data from thousands of devices. The data is ingested in near real-time, and queries will involve filtering by device ID and timestamp. The solution must minimize storage costs while supporting interactive queries. Which TWO Azure data storage options are most appropriate?
easy- A.Azure Table Storage
- B.Azure Cosmos DB
- ✓ C.Azure Blob Storage
- ✓ D.Azure Data Lake Storage Gen2
- E.Azure SQL Database
Why C: Azure Blob Storage (C) is correct because it provides a cost-effective, scalable object store for semi-structured IoT data, supporting near real-time ingestion via REST APIs or SDKs and enabling interactive queries through Azure Data Lake Storage Gen2's hierarchical namespace and integration with query engines like Azure Synapse Serverless SQL or PolyBase. Azure Data Lake Storage Gen2 (D) is correct as it builds on Blob Storage with a hierarchical namespace, optimized for analytics workloads and interactive queries using tools like Azure Synapse or Databricks, while minimizing costs through tiered storage and lifecycle management.
Variation 2. You are designing a data storage solution for a retail company that needs to store semi-structured JSON data from IoT sensors. The data is ingested continuously and must support both real-time analytics and batch processing. Which Azure data store should you recommend?
medium- ✓ A.Azure Data Lake Storage Gen2
- B.Azure Blob Storage
- C.Azure SQL Database
- D.Azure Cosmos DB
Why A: Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical file system with the scalability and low cost of Azure Blob Storage, making it ideal for storing semi-structured JSON data from IoT sensors. It supports both real-time analytics (via services like Azure Stream Analytics or Apache Spark) and batch processing (via tools like Azure Data Factory or PolyBase) without data movement, and it natively handles JSON files with schema-on-read capabilities.
Keep practising
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Last reviewed: Jun 24, 2026
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