- A
Azure Cosmos DB SQL API with a collection partitioned by timestamp.
Why wrong: Cosmos DB provides low-latency access and flexible queries, but it is significantly more expensive than Azure Table Storage for high-volume log ingestion. The cost per GB is much higher, and write throughput is billed per RU.
- B
Azure Blob Storage with append blobs and a custom index for timestamp queries.
Why wrong: Append blobs are good for sequential writes, but querying by timestamp across billions of small entries would require a separate index and is less efficient than Table Storage's built-in row key indexing.
- C
Azure Table Storage with a partition key combining device ID and date, and row key as timestamp.
Correct. Table Storage provides high throughput at low cost. By designing the partition key appropriately, you can achieve efficient range queries on timestamps and handle billions of entries.
- D
Azure SQL Database with a clustered columnstore index.
Why wrong: SQL Database is not designed for such high-volume unstructured log data at low cost. It would be expensive and the schema would need to be rigid, while Table Storage provides schema-less tables.
Quick Answer
The answer is Azure Table Storage with a partition key combining device ID and date, and row key as timestamp. This design is correct because it leverages Table Storage’s schema-free, key-value architecture to maximize write throughput by distributing billions of small log entries across many partitions, while the row key as timestamp enables fast, server-side range queries over chronological data without the overhead of a separate index. On the AZ-204 exam, this scenario tests your understanding of how to balance partition distribution for high-volume writes against the need for efficient range scans—a common trap is choosing Cosmos DB for its indexing, which would increase cost and latency for this simple log workload. Remember the memory tip: “Partition by device and date, row key by time—logs scale fine.”
AZ-204 Develop for Azure storage Practice Question
This AZ-204 practice question tests your understanding of develop for azure 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 need to store billions of small log entries (each ~200 bytes) generated from multiple IoT devices. The logs are written in chronological order and are rarely updated. You need to run queries that scan large ranges of data by timestamp each day. You want to maximize write throughput and minimize storage costs. Which Azure Storage solution should you choose?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Table Storage with a partition key combining device ID and date, and row key as timestamp.
Azure Table Storage is ideal for this scenario because it supports high-volume, low-cost storage of structured log data with efficient range queries. By using a partition key of device ID combined with date, you distribute writes across partitions for high throughput, while the row key as timestamp enables fast, server-side range scans over chronological data without the overhead of a separate index.
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 Cosmos DB SQL API with a collection partitioned by timestamp.
Why it's wrong here
Cosmos DB provides low-latency access and flexible queries, but it is significantly more expensive than Azure Table Storage for high-volume log ingestion. The cost per GB is much higher, and write throughput is billed per RU.
- ✗
Azure Blob Storage with append blobs and a custom index for timestamp queries.
Why it's wrong here
Append blobs are good for sequential writes, but querying by timestamp across billions of small entries would require a separate index and is less efficient than Table Storage's built-in row key indexing.
- ✓
Azure Table Storage with a partition key combining device ID and date, and row key as timestamp.
Why this is correct
Correct. Table Storage provides high throughput at low cost. By designing the partition key appropriately, you can achieve efficient range queries on timestamps and handle billions of entries.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure SQL Database with a clustered columnstore index.
Why it's wrong here
SQL Database is not designed for such high-volume unstructured log data at low cost. It would be expensive and the schema would need to be rigid, while Table Storage provides schema-less tables.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Cosmos DB for its indexing and query capabilities, overlooking the fact that Table Storage provides native, cost-effective range queries on the row key without additional indexing costs, making it the optimal choice for high-volume, low-cost log storage with timestamp-based scans.
Detailed technical explanation
How to think about this question
Azure Table Storage uses a key-value store with a partition key and row key forming a composite primary key; queries that specify both keys or a range on the row key within a partition are served directly from the index without table scans. The partition key design (device ID + date) ensures even load distribution across partition servers, avoiding hot spots, while the row key (timestamp) leverages the lexicographic ordering of strings to support efficient range queries using 'greater than' and 'less than' filters. In practice, this pattern is commonly used for IoT telemetry pipelines where logs are written once and queried by time windows, achieving write throughput of up to 20,000 entities per second per partition.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Develop for Azure storage — study guide chapter
Learn the concepts, then practise the questions
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Develop for Azure storage practice questions
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Develop for Azure storage — This question tests Develop for Azure storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Table Storage with a partition key combining device ID and date, and row key as timestamp. — Azure Table Storage is ideal for this scenario because it supports high-volume, low-cost storage of structured log data with efficient range queries. By using a partition key of device ID combined with date, you distribute writes across partitions for high throughput, while the row key as timestamp enables fast, server-side range scans over chronological data without the overhead of a separate index.
What should I do if I get this AZ-204 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 AZ-204
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 need to store billions of small log entries (each ~200 bytes) written in chronological order from multiple producers. The logs are read sequentially in bulk once per day. You need to maximize write throughput and minimize storage costs. Which Azure Storage solution should you choose?
hard- ✓ A.Append Blob in Blob Storage
- B.Block Blob in Blob Storage with high block count
- C.Azure Data Lake Storage Gen2 with hierarchical namespace
- D.Azure Files with SMB protocol
Why A: Append Blob in Blob Storage is optimized for append operations, making it ideal for writing small log entries in chronological order from multiple producers. It provides high write throughput because each append operation is atomic and can be performed concurrently, and it minimizes storage costs by storing data in a cost-effective blob tier without the overhead of indexing or metadata management required by other solutions.
Variation 2. You need to store billions of small telemetry data entries (each ~100 bytes) from IoT devices. The data is written once and rarely updated. You need to run analytical queries on the last 30 days of data daily. The queries scan large ranges of data by timestamp and require sub-second response times. You need the lowest storage cost while meeting query latency requirements. Which Azure Storage solution should you use?
hard- A.Azure Blob Storage with hot access tier and Data Lake Storage Gen2.
- B.Azure Table Storage with a timestamp partition key.
- C.Azure Cosmos DB with SQL API and automatic indexing.
- ✓ D.Azure Blob Storage with cool access tier and Azure Data Lake Storage Gen2.
Why D: Option D is correct because Azure Blob Storage with the cool access tier provides the lowest storage cost for data that is written once and rarely accessed, while Azure Data Lake Storage Gen2 enables hierarchical namespace and POSIX-like access, allowing efficient analytical queries on large timestamp-ranged data with sub-second response times via partitioning and parallel processing.
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Last reviewed: Jun 11, 2026
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