- A
Implement request batching to increase throughput and reduce the number of inference requests.
Why wrong: Batching could increase memory usage and latency further.
- B
Increase the instance type to a more memory-intensive instance like ml.p3.8xlarge to handle memory spikes.
Why wrong: While it might alleviate symptoms, it doesn't diagnose the root cause and could be cost-ineffective.
- C
Set up SageMaker Model Monitor to track data drift and model quality metrics.
Why wrong: Model Monitor is for monitoring model performance over time, not real-time performance diagnostics.
- D
Enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference.
Debugger can provide detailed profiling to pinpoint resource contention or memory issues in the model or framework.
Quick Answer
The answer is to enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference. This is the correct first step because the intermittent latency spikes and memory surges, despite low CPU and normal invocation counts, suggest a subtle resource contention or garbage collection issue that standard CloudWatch metrics cannot isolate. Debugger’s built-in profiling hooks capture per-operator memory and CPU usage, allowing you to pinpoint whether the MXNet model’s memory allocation pattern or an external dependency call is causing the periodic stalls. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between performance monitoring tools: CloudWatch gives aggregate metrics, while Debugger provides granular profiling for root-cause analysis. A common trap is jumping to resizing the instance or adding batching, which can mask or worsen memory pressure. Memory tip: think “Debugger digs deeper” — when latency spikes but CPU is low, profile memory, don’t resize.
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 healthcare company uses Amazon SageMaker to deploy a real-time inference endpoint for a diagnostic model. The endpoint is configured with a single ml.p3.2xlarge instance. The model processes patient data and returns a risk score. Recently, the endpoint has been experiencing intermittent 504 errors along with increased latency. The team uses Amazon CloudWatch to monitor the endpoint's InvocationsPerInstance and ModelLatency metrics. They observe that InvocationsPerInstance is well below the throttling threshold, but ModelLatency shows periodic spikes lasting 5-10 seconds. The endpoint's CPU utilization remains below 60%, but memory utilization occasionally spikes to 90% during those spikes. The team has checked the inference code and found no obvious memory leaks or performance bottlenecks in the custom logic. The model itself is a deep neural network hosted using Apache MXNet. The team suspects that the issue might be related to resource contention or an external dependency. What should the team do FIRST to diagnose and resolve the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference.
Option B is correct because the symptoms point to a possible memory contention issue, and enabling detailed profiling for memory and CPU can identify the root cause. Option A is wrong because increasing instance size might mask the problem without identifying it. Option C is wrong because request batching can increase memory usage and may worsen the issue. Option D is wrong because Model Monitor is for data drift, not performance diagnostics.
Key principle: Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement request batching to increase throughput and reduce the number of inference requests.
Why it's wrong here
Batching could increase memory usage and latency further.
- ✗
Increase the instance type to a more memory-intensive instance like ml.p3.8xlarge to handle memory spikes.
Why it's wrong here
While it might alleviate symptoms, it doesn't diagnose the root cause and could be cost-ineffective.
- ✗
Set up SageMaker Model Monitor to track data drift and model quality metrics.
Why it's wrong here
Model Monitor is for monitoring model performance over time, not real-time performance diagnostics.
- ✓
Enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference.
Why this is correct
Debugger can provide detailed profiling to pinpoint resource contention or memory issues in the model or framework.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
CIDR notation defines the prefix length.
Common exam traps
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Detailed technical explanation
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Key takeaway
Count usable hosts — not total addresses — and remember that the network and broadcast addresses are not available to hosts in standard IPv4 subnets.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related MLA-C01 subnetting questions on CIDR, address ranges, and subnet selection.
- →
ML Solution Monitoring, Maintenance and Security — study guide chapter
Learn the concepts, then practise the questions
- →
ML Solution Monitoring, Maintenance and Security practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — CIDR notation defines the prefix length..
What is the correct answer to this question?
The correct answer is: Enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference. — Option B is correct because the symptoms point to a possible memory contention issue, and enabling detailed profiling for memory and CPU can identify the root cause. Option A is wrong because increasing instance size might mask the problem without identifying it. Option C is wrong because request batching can increase memory usage and may worsen the issue. Option D is wrong because Model Monitor is for data drift, not performance diagnostics.
What should I do if I get this MLA-C01 question wrong?
Review block sizes, usable host formulas (2^n − 2), and how to find network and broadcast addresses for /24 through /30. Then practise related MLA-C01 subnetting questions on CIDR, address ranges, and subnet selection.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
CIDR notation defines the prefix length.
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 →
Keep practising
More MLA-C01 practice questions
- A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality…
- A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accura…
- A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices shou…
- A company is using SageMaker to train a neural network for image classification. The training job is taking too long. Th…
- A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target vari…
- A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multipl…
Last reviewed: Jun 23, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.