MLS-C01 Machine Learning Implementation and Operations Practice Test 17 — 15 Questions

Question 1

A company is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. Which IAM policy configuration is required to allow SageMaker to access the data?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Add a bucket policy allowing access from the SageMaker execution role ARN, and ensure the SageMaker execution role has an IAM policy allowing s3:GetObject on the bucket.. Cross-account access requires the SageMaker execution role to have an IAM policy allowing access to the S3 bucket, and the S3 bucket policy must grant access to that role. Option A is wrong because SageMaker cannot assume a role in another account without proper trust policy. Option C is wrong because the data owner's role cannot be used directly. Option D is wrong because SageMaker does not use the data owner's IAM user credentials.

Answer

Add a bucket policy that allows s3:GetObject for the SageMaker execution role's ARN.

Answer

Create an IAM user in the data owner's account and use its credentials in SageMaker.

Answer

Use the data owner's IAM role as the SageMaker execution role.

Question 2

A machine learning engineer is deploying a model on SageMaker and needs to ensure that the endpoint can handle a sudden spike in traffic. The engineer expects traffic to increase by 10x during a promotional event. Which scaling strategy should be used?

Accepted Answer

Use scheduled scaling to add instances before the event, combined with dynamic scaling for the remaining duration.. The correct answer. Scheduled scaling allows you to add instances before the expected traffic spike, ensuring capacity is ready when needed. Combined with dynamic scaling (e.g., based on CPU utilization or request count), you can handle unexpected additional load during the event. Option A is wrong because a single large instance is a single point of failure and may not provide sufficient throughput for a 10x spike. Option B is wrong because manual scaling requires human intervention and may not react fast enough. Option C is wrong because relying solely on dynamic scaling may not scale up quickly enough for a sudden 10x increase, as there is a lag in metrics and scaling actions.

Answer

Use a single large instance type instead of multiple smaller instances.

Answer

Manually increase the instance count before the event.

Answer

Use only dynamic scaling based on the average latency metric.

Question 3

A data scientist is using Amazon SageMaker to train a model and wants to use a custom Docker container for training. The container requires access to a private Amazon ECR repository. Which IAM role configuration is needed?

Accepted Answer

Attach an IAM policy to the SageMaker execution role that allows ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, and ecr:GetAuthorizationToken for the ECR repository.. The SageMaker execution role must have an IAM policy that includes ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, and ecr:GetAuthorizationToken for the ECR repository. This is achieved by attaching an IAM policy to the SageMaker execution role, as described in Option A. Option B is incorrect because using the AWS account owner's role is not appropriate and would grant excessive permissions. Option C is incorrect because IAM users are not used for SageMaker execution roles; roles are used instead. Option D is incorrect because bucket policies apply to S3 buckets, not ECR repositories; ECR uses resource-based policies on the repository itself.

Answer

Use the AWS account owner's IAM role as the SageMaker execution role.

Answer

Create a new IAM user with ECR access and store credentials in SageMaker.

Answer

Add a bucket policy to the ECR repository allowing access from the SageMaker execution role.

Question 4

A company wants to use Amazon SageMaker to train a model using data that is updated daily. The training data is stored in an S3 bucket, and the team wants to automate the training process whenever new data arrives. Which AWS service should be used to trigger the SageMaker training job?

Accepted Answer

AWS Lambda triggered by S3 event notifications. AWS Lambda can be triggered by S3 event notifications (e.g., object creation). The Lambda function can then start the SageMaker training job using the AWS SDK, automating the process when new data arrives. Option B (Amazon CloudWatch Events) is incorrect because CloudWatch Events can schedule events based on time or AWS API calls but cannot directly react to S3 object creation without additional services. Option C (Amazon SQS) is incorrect because SQS is a message queue service; it does not natively trigger from S3 events without an intermediary like Lambda, which would still be the trigger. Option D (AWS Step Functions with a scheduled trigger) is incorrect because a scheduled trigger is time-based, not event-driven; Step Functions would need an S3 event to start, and that event would typically come via Lambda.

Answer

Amazon CloudWatch Events

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions with a scheduled trigger

Question 5

A company is deploying a machine learning model on Amazon SageMaker. The model needs to be updated frequently with new versions. The team wants to minimize downtime and test the new model version before routing all traffic to it. Which TWO strategies should be used together?

Accepted Answer

Use Amazon SageMaker canary deployment.. The correct answers are D (canary deployment) and E (blue/green deployment). In Amazon SageMaker, blue/green deployment allows you to deploy a new model version alongside the existing one (blue) and then shift traffic gradually. Canary deployment is a feature of SageMaker that routes a small percentage of traffic to the new version for testing before shifting more. Together, these strategies minimize downtime and allow testing. Option A (rolling update) is not directly supported in SageMaker for endpoints; SageMaker uses deployment variants. Option B (multi-model endpoint) is for hosting multiple models on the same endpoint but does not provide traffic shifting for updates. Option C (A/B testing) in SageMaker is typically achieved using production variants with traffic weights, but the specific feature for gradual traffic shifting is called canary deployment, so option C is incorrect as stated.

Answer

Use a rolling update strategy.

Answer

Use a multi-model endpoint.

Answer

Use Amazon SageMaker A/B testing.

Question 6

A data scientist is training a model using Amazon SageMaker and wants to reduce the training time. The training job uses a single GPU instance. Which THREE actions can reduce training time?

Accepted Answer

Use distributed training across multiple GPU instances.. Options A, B, and C are correct. Distributed training across multiple GPU instances (A) leverages parallelism to reduce training time. Pipe input mode (B) streams data directly from S3, reducing I/O wait time compared to File mode which downloads data first. Using a larger instance type with more GPU memory and compute (C) provides more processing power, allowing faster training. Option D is incorrect because increasing the amount of training data typically increases training time, not reduces it. Option E is incorrect because reducing batch size can lead to more iterations and longer training time, though it may sometimes affect convergence.

Answer

Increase the amount of training data.

Answer

Reduce the batch size.

Question 7

A company wants to deploy a machine learning model on Amazon SageMaker and needs to monitor the model's performance in production. Which TWO AWS services can be used to set up monitoring?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is correct because it provides comprehensive monitoring for SageMaker endpoints, including metrics like latency, invocation counts, and error rates. It can also trigger alarms and dashboards for performance degradation. Amazon SageMaker Model Monitor is correct because it specifically detects data drift, feature attribution drift, and quality issues in production models by analyzing inference data against a baseline.

Answer

AWS X-Ray

Answer

Amazon Inspector

Answer

AWS Config

Question 8

A data scientist is deploying a PyTorch model to Amazon SageMaker for real-time inference. The model runs on a large instance but inference latency is too high. Which action is MOST likely to reduce latency without sacrificing accuracy?

Accepted Answer

Compile the model using SageMaker Neo. SageMaker Neo compiles the trained model into an optimized runtime using Apache TVM, applying graph-level optimizations, operator fusion, and memory layout transformations specifically tuned for the target hardware. This reduces inference latency by improving computational efficiency without altering the model's weights or architecture, thus preserving accuracy.

Answer

Switch from a GPU instance to a CPU instance

Answer

Quantize the model weights from FP32 to INT8

Answer

Deploy the model to a multi-model endpoint

Question 9

A team is using Amazon SageMaker to train a linear regression model on a dataset with 10 features. After training, they notice the model has high bias. Which action is MOST likely to reduce bias?

Accepted Answer

Add polynomial features to capture non-linear relationships. High bias indicates underfitting, which can be reduced by adding more features or increasing model complexity. Option A reduces risk of overfitting, not bias. Option B increases regularization, which increases bias. Option C reduces data, potentially increasing bias.

Answer

Increase the regularization parameter lambda

Answer

Add L2 regularization

Answer

Use a smaller training dataset

Question 10

A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is failing with a 'ResourceLimitExceeded' error. The engineer checks the account limits and sees that the current limit for the instance type is 2, and they are already using 2 instances for other jobs. Which approach would resolve the issue MOST cost-effectively?

Accepted Answer

Use a different instance type that is available and has sufficient capacity. Using a different instance type within the same family often has separate limits. Option A increases cost. Option C may not resolve if the limit is account-wide. Option D changes the request, not the limit.

Answer

Request a service limit increase for the current instance type

Answer

Use a managed spot training instead of on-demand

Answer

Stop the other training jobs to free up resources

Question 11

A data scientist is using Amazon SageMaker to train a model. The training job uses a custom Docker image stored in Amazon ECR. The training job fails with an error 'CannotPullContainerError'. Which TWO actions should the data scientist take to resolve this issue? (Choose TWO.)

Accepted Answer

Confirm that the image tag exists in the ECR repository. Options B and C are correct because the error 'CannotPullContainerError' typically indicates that the container image cannot be pulled from ECR. This can occur if the image tag does not exist in the repository (B) or if the IAM role used by the SageMaker training job does not have the necessary permissions (e.g., ecr:GetDownloadUrlForLayer, ecr:BatchGetImage) to pull the image (C). Option A is not required; custom images can be used. Option D, increasing the timeout, would not resolve a pull issue. Option E, internet access, is not necessary because SageMaker can access ECR within the AWS network.

Answer

Use a public Docker image instead of a custom one

Answer

Increase the training job timeout

Answer

Ensure the training instance has internet access

Question 12

A company is deploying a machine learning model using Amazon SageMaker. To reduce costs, they want to use SageMaker Managed Spot Training. Which THREE conditions must be met for the training job to use spot instances? (Choose THREE.)

Accepted Answer

The training job must be able to handle interruptions gracefully. For SageMaker Managed Spot Training, the training job must be able to handle interruptions (B) because spot instances can be reclaimed. The chosen instance type must be available in the spot market (C). Additionally, the training script should save checkpoints to S3 periodically (D) to resume training if interrupted. Option A is incorrect because spot training is for training, not deploying endpoints. Option E is not required; training can run inside or outside a VPC.

Answer

The model must be deployed to a serverless endpoint

Answer

The training job must be configured to run in a VPC

Question 13

A data engineer is building a data pipeline for a machine learning project using Amazon SageMaker. The raw data is stored in Amazon S3. Which TWO steps are essential to ensure data privacy and security before training? (Choose TWO.)

Accepted Answer

Encrypt the data at rest using S3 server-side encryption. Options C and E are correct because data privacy and security require encryption at rest (e.g., S3 server-side encryption) and secure network access (e.g., using SageMaker in a VPC with VPC endpoints). Option C ensures data is encrypted when stored in S3. Option E prevents data from traversing the public internet and allows fine-grained access control. Options A, B, and D are not essential for privacy/security: A (bucket policy) is a means of access control but not as fundamental as encryption; B (versioning) protects against accidental deletion, not privacy; D (Transfer Acceleration) is a performance feature, not a security measure.

Answer

Create a bucket policy that restricts access to the data scientist's IAM role only

Answer

Enable versioning on the S3 bucket

Answer

Use S3 Transfer Acceleration for faster uploads

Question 14

An IAM policy attached to a SageMaker execution role is shown in the exhibit. When a data scientist tries to create a training job that writes logs to CloudWatch Logs, the job fails. What is the MOST likely reason?

Accepted Answer

The policy lacks permissions for CloudWatch Logs actions. The IAM policy attached to the SageMaker execution role does not include permissions for CloudWatch Logs actions (e.g., CreateLogGroup, CreateLogStream, PutLogEvents), which are required to write logs during training job execution. Option A is incorrect because the API version is not relevant to permission issues. Option B is incorrect because the problem is not related to S3 bucket policy; the training job fails due to logging, not data access. Option D is incorrect because the policy does not contain an explicit deny for SageMaker actions, but the lack of CloudWatch Logs permissions causes the failure.

Answer

The policy does not specify the SageMaker API version

Answer

The S3 bucket policy denies access to the training job

Answer

The policy has an implicit deny for SageMaker actions

Question 15

An engineer runs the AWS CLI command in the exhibit to create a SageMaker endpoint configuration. The endpoint is created successfully, but when invoked, the inference response is slow. The engineer wants to test with a different instance type. Which action should the engineer take?

Accepted Answer

Update the endpoint configuration with the new instance type and then update the endpoint. To change the instance type for an existing SageMaker endpoint, you must first update the endpoint configuration with the new instance type, then update the endpoint to use the updated configuration. Option D describes this correct procedure. Option B is incorrect because the update-endpoint API does not directly accept an instance type parameter; it updates the endpoint to use a new endpoint configuration. Option A creates a new endpoint, which is less efficient. Option C deletes the endpoint unnecessarily.

Answer

Create a new endpoint configuration and use it to create a new endpoint

Answer

Modify the existing endpoint directly using the update-endpoint API with a new instance type parameter

Answer

Delete the endpoint and create a new one with the desired instance type