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MLA-C01 · topic practice

ML Solution Monitoring, Maintenance and Security practice questions

Practise AWS Certified Machine Learning Engineer Associate MLA-C01 ML Solution Monitoring, Maintenance and Security practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: ML Solution Monitoring, Maintenance and Security
Practice 10 questionsBrowse domain →

What the exam tests

What to know about ML Solution Monitoring, Maintenance and Security

ML Solution Monitoring, Maintenance and Security questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common ML Solution Monitoring, Maintenance and Security exam traps

  • ▸Answering from memory before reading the full scenario.
  • ▸Missing a constraint such as cost, availability, security, scope or command context.
  • ▸Choosing a broad answer when the question asks for the most specific fix.
  • ▸Ignoring why the wrong options are tempting.

Practice set

ML Solution Monitoring, Maintenance and Security questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice
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A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?

Question 2mediummultiple choice
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A data science team is using Amazon SageMaker to train and deploy a binary classification model. They want to continuously monitor the model for data drift in production. Which combination of AWS services and SageMaker features should they use to implement automated drift detection with minimal operational overhead?

Question 3hardmultiple choice
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A financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?

Question 4mediummulti select
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A machine learning team is building a CI/CD pipeline for model deployment using Amazon SageMaker. They need to ensure that all model artifacts are encrypted at rest and in transit, and that access to the models is controlled via IAM. Which TWO actions should the team take to meet these requirements? (Choose TWO.)

Question 5hardmulti select
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A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)

Question 6easymulti select
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A company is using Amazon SageMaker to host a real-time inference endpoint. They want to restrict access to the endpoint to only a specific VPC and require authentication using AWS IAM. Which TWO configuration steps should they take to achieve this? (Choose TWO.)

Question 7mediummultiple choice
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A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

Question 8hardmultiple choice
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A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

Question 9easymultiple choice
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A company is using Amazon SageMaker to train a model on sensitive customer data. The security team requires that all data be encrypted in transit and at rest, and that the training job does not have internet access. Which configuration should the team use to meet these requirements?

Question 10easymultiple choice
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A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

Question 11easymultiple choice
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A data science team deploys a regression model using Amazon SageMaker. After one week, the model's prediction accuracy drops significantly. The team needs to detect this degradation automatically and trigger retraining. Which AWS service should they use to monitor the model's performance over time and set up alerts?

Question 12mediummultiple choice
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A company uses Amazon SageMaker to host a real-time inference endpoint for a fraud detection model. The endpoint is deployed with three instances of ml.m5.large. The model processes each request in about 200 ms. Lately, users report occasional timeouts (requests taking >5 seconds). The team suspects model drift or data skew. What is the MOST likely cause and solution?

Question 13hardmultiple choice
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A machine learning engineer deploys a model to an Amazon SageMaker endpoint with data capture enabled. The endpoint uses a production variant with initial instance count of 2. After a week, they notice that the captured data is not being sent to the specified Amazon S3 bucket. The IAM role used by the endpoint has the following policy attached. What is the MOST likely reason for the failure?

Question 14easymultiple choice
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A company uses Amazon Rekognition to moderate user-generated images. They want to set up a monitoring system that alerts the team if the number of inappropriate images flagged by the model exceeds a threshold. Which combination of AWS services should they use?

Question 15mediummultiple choice
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A team deploys a PyTorch model on Amazon SageMaker for real-time inference. They notice that inference latency is higher than expected. They suspect the serialization format used for input data is inefficient. Which approach would MOST likely reduce latency?

Question 16hardmultiple choice
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A financial services company deploys a credit risk model using an Amazon SageMaker endpoint with data capture enabled. The model uses a custom container. The compliance team requires that all inference requests and responses are logged to an S3 bucket with server-side encryption using AWS KMS. The IAM role for the endpoint has the following policy. What must be added to meet the compliance requirement?

Question 17easymulti select
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Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

Question 18mediummulti select
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Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)

Question 19hardmultiple choice
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A company operates an e-commerce platform that uses a machine learning model to recommend products to users. The model is deployed on an Amazon SageMaker endpoint with automatic scaling enabled based on average CPU utilization. The model was trained on historical data and is updated weekly. Recently, the platform experienced a flash sale event that caused a sudden spike in traffic. During the event, the endpoint's latency increased dramatically, and many requests timed out. After the event, the team reviews the CloudWatch metrics and notices that the CPU utilization never exceeded 70%, and the scaling policy was triggered but instances took several minutes to become available. The team wants to prevent similar issues in future flash sales. Which course of action would be MOST effective?

Question 20mediummultiple choice
Read the full NAT/PAT explanation →

A healthcare company deploys a model that predicts patient readmission risk. The model is deployed using a SageMaker real-time endpoint with data capture enabled. The compliance team requires that all inference data be encrypted at rest in S3 using AWS KMS with a customer managed key. The team has configured the endpoint to use an IAM role that includes the necessary KMS permissions. However, after deployment, the captured data is not being written to the S3 bucket. The team checks the CloudWatch logs for the endpoint and finds no errors. The S3 bucket policy is as follows:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Principal": "*",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Condition": {
        "Bool": {

"aws:SecureTransport": "false"

}
      }
    }
  ]
}

The bucket also has a default KMS key. What is the MOST likely reason that the captured data is not being written?

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Frequently asked questions

What does the MLA-C01 exam test about ML Solution Monitoring, Maintenance and Security?
ML Solution Monitoring, Maintenance and Security questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just ML Solution Monitoring, Maintenance and Security questions in a focused session?
Yes — the session launcher on this page draws every question from the ML Solution Monitoring, Maintenance and Security domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other MLA-C01 topics?
Use the topic links above to move to related areas, or go back to the MLA-C01 question bank to see all topics.
Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the MLA-C01 exam covers. They are not copied from any real exam or dump site.