The correct answer is to increase the read chunk size, because high latency from small read requests is a classic symptom of excessive API overhead. Each tiny request to Cloud Storage incurs a fixed network round-trip and authentication cost, so batching data into larger reads—using range requests or larger buffer sizes—dramatically reduces the total number of API calls, lowering cumulative latency and improving throughput. On the Google Professional Cloud Developer exam, this scenario tests your understanding of Cloud Storage performance tuning under load, often appearing as a trap where developers mistakenly try to add caching or switch storage classes instead of addressing the root cause of too many small requests. A common memory tip is to think of it like drinking water: sipping from a cup one drop at a time takes forever, but taking a full gulp finishes the job quickly. Remember: fewer, larger reads beat many tiny ones.
PCD Building and testing applications Practice Question
This PCD practice question tests your understanding of building and testing applications. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An application running on Compute Engine uses Cloud Storage for storing user-uploaded images. During load testing, the application experiences high latency when reading images. The developer suspects that the application is making too many small read requests. Which approach should the developer take to optimize performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the read size to reduce the number of API requests.
Option C is correct because the high latency is caused by many small read requests, each incurring API overhead. By increasing the read size (e.g., reading larger chunks or using range requests), the application reduces the number of API calls, which lowers cumulative latency and improves throughput. This directly addresses the root cause of excessive small reads.
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.
✗
Enable Cloud CDN to cache the images at edge locations.
Why it's wrong here
Cloud CDN caches content but is intended for content delivery, not for reducing small read overhead in a backend application.
✗
Rewrite the objects to use a different storage class.
Why it's wrong here
Storage class affects cost and availability, not read latency for small requests.
✓
Increase the read size to reduce the number of API requests.
Why this is correct
Reading larger chunks reduces the number of HTTP requests and improves throughput, especially for sequential access patterns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Mount the Cloud Storage bucket using Cloud Storage FUSE and read files from the local filesystem.
Why it's wrong here
Cloud Storage FUSE may increase latency for random reads and is not designed for high-throughput scenarios.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that caching (Cloud CDN) or filesystem mounting (FUSE) solves performance issues caused by small read patterns, when the real fix is to reduce the number of API calls by increasing the read size.
Trap categories for this question
Scenario analysis trap
Cloud Storage FUSE may increase latency for random reads and is not designed for high-throughput scenarios.
Detailed technical explanation
How to think about this question
Cloud Storage API calls have a fixed overhead per request (e.g., HTTPS handshake, authentication, metadata). By using larger read sizes (e.g., reading 4 MB instead of 256 KB), the application amortizes this overhead over more data. The recommended practice is to use range requests (HTTP Range header) to fetch larger chunks and buffer them locally, which can reduce latency by up to 10x in high-latency scenarios. This is especially important for object storage, where random small reads are inefficient compared to sequential large reads.
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.
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.
Building and testing applications — This question tests Building and testing applications — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the read size to reduce the number of API requests. — Option C is correct because the high latency is caused by many small read requests, each incurring API overhead. By increasing the read size (e.g., reading larger chunks or using range requests), the application reduces the number of API calls, which lowers cumulative latency and improves throughput. This directly addresses the root cause of excessive small reads.
What should I do if I get this PCD 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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