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Question 934 of 1,786

Data Store Management →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to resize the DB instance to a larger type, such as db.r5.xlarge, because the consistently high queue depth above 32 and write latency exceeding 50 ms signal that the current instance is CPU or I/O constrained for the write workload. This constraint stems from the frequent INSERT and UPDATE operations on a 50-million-row table, where a larger instance provides more vCPUs and memory to process concurrent writes, directly reducing queue depth and latency without requiring application code changes. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your ability to distinguish between scaling compute resources versus storage, as a common trap is to increase Provisioned IOPS when the bottleneck is actually compute capacity. Remember the memory tip: “Queue depth above 32? Time to grow the instance, not the IOPS.”

DEA-C01 Data Store Management Practice Question

This DEA-C01 practice question tests your understanding of data store management. 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.

A company runs a MySQL-compatible Amazon Aurora database for its e-commerce platform. The database experiences high write latency during peak hours. The application performs frequent INSERT and UPDATE operations on a table with 50 million rows. The DB instance is db.r5.large with 500 GB of Provisioned IOPS storage. A recent performance analysis shows that the average queue depth is consistently above 32 and write latency exceeds 50 ms. The company needs to reduce write latency without changing the application code. What should a data engineer do?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Enable Aurora Auto Scaling to automatically add reader instances.
Why wrong: Auto Scaling adds read replicas, which does not reduce write latency on the primary instance.
B
Convert the cluster to Aurora Serverless v2 to automatically scale compute capacity.
Why wrong: Aurora Serverless v2 scales compute, but may introduce latency during scaling events and does not directly address the specific performance issue with Provisioned IOPS.
C
Resize the DB instance to a larger instance type such as db.r5.xlarge.
Increasing instance size provides more CPU and memory, reducing queue depth and write latency.
D
Create a read replica and configure the application to offload read queries.
Why wrong: Read replicas offload reads but do not affect write performance on the primary.

Full breakdown with real-world context →

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

✓

Resize the DB instance to a larger instance type such as db.r5.xlarge.

Option C is correct because the high queue depth (consistently above 32) and write latency exceeding 50 ms indicate that the current db.r5.large instance is CPU or I/O constrained for the write workload. Resizing to a larger instance type such as db.r5.xlarge increases the available vCPUs, memory, and network bandwidth, which directly reduces queue depth and write latency by allowing more concurrent write operations to be processed. This solution does not require application code changes and addresses the root cause of insufficient compute capacity for the frequent INSERT and UPDATE operations on the 50-million-row table.

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 Aurora Auto Scaling to automatically add reader instances.
Why it's wrong here
Auto Scaling adds read replicas, which does not reduce write latency on the primary instance.
✗
Convert the cluster to Aurora Serverless v2 to automatically scale compute capacity.
Why it's wrong here
Aurora Serverless v2 scales compute, but may introduce latency during scaling events and does not directly address the specific performance issue with Provisioned IOPS.
✓
Resize the DB instance to a larger instance type such as db.r5.xlarge.
Why this is correct
Increasing instance size provides more CPU and memory, reducing queue depth and write latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create a read replica and configure the application to offload read queries.
Why it's wrong here
Read replicas offload reads but do not affect write performance on the primary.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse read scaling solutions (Auto Scaling, read replicas) with write performance improvements, or assume that Aurora Serverless v2 automatically solves all performance issues without considering that write latency is often tied to instance size and storage configuration.

Detailed technical explanation

How to think about this question

The queue depth metric represents the number of I/O requests waiting to be processed by the storage subsystem; a value consistently above 32 indicates that the instance's I/O bandwidth or CPU is saturated. In Aurora MySQL, write operations involve a quorum write to six storage nodes across three Availability Zones, and the db.r5.large instance has a baseline EBS bandwidth of up to 4,750 Mbps, but the instance's CPU and memory limits can throttle the number of concurrent write operations. Resizing to db.r5.xlarge doubles the vCPUs (from 2 to 4) and memory (from 16 GB to 32 GB), which increases the instance's ability to handle write-intensive workloads and reduces the queue depth.

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Resize the DB instance to a larger instance type such as db.r5.xlarge. — Option C is correct because the high queue depth (consistently above 32) and write latency exceeding 50 ms indicate that the current db.r5.large instance is CPU or I/O constrained for the write workload. Resizing to a larger instance type such as db.r5.xlarge increases the available vCPUs, memory, and network bandwidth, which directly reduces queue depth and write latency by allowing more concurrent write operations to be processed. This solution does not require application code changes and addresses the root cause of insufficient compute capacity for the frequent INSERT and UPDATE operations on the 50-million-row table.

What should I do if I get this DEA-C01 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.

About these practice questions

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Same concept, more angles

1 more ways this is tested on DEA-C01

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. A company is running a production Amazon Aurora PostgreSQL database. The database experiences high write latency during peak hours. The data engineer suspects that the issue is due to a large number of small transactions. Which action would MOST effectively reduce write latency?

hard

A.Enable parallel query for the database
✓ B.Increase the instance size and use Provisioned IOPS storage
C.Enable Aurora Auto Scaling for read replicas
D.Enable Performance Insights to identify the bottleneck

Why B: Increasing the instance size and using Provisioned IOPS storage directly addresses high write latency by providing more CPU and memory resources to handle transaction processing, while Provisioned IOPS ensures consistent, low-latency I/O for write operations. This is the most effective action because small transactions create high I/O demand, and scaling up the instance with dedicated IOPS reduces contention and write queue depth.

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