MLS-C01 Machine Learning Implementation and Operations Practice Test 21 — 15 Questions

Question 1

A team uses SageMaker to train a deep learning model. They notice the training job is using only a fraction of the GPU memory. Which configuration change would most improve GPU utilization?

Accepted Answer

Increase the batch size in the training script. Option A is correct: increasing batch size uses more GPU memory and improves utilization. Option B (reducing batch size) would decrease utilization. Option C (using single GPU) would not help. Option D (spot training) does not affect utilization.

Answer

Decrease the batch size to reduce memory fragmentation

Answer

Use a single GPU instead of multiple GPUs

Answer

Enable SageMaker Managed Spot Training

Question 2

A company wants to serve a scikit-learn model via SageMaker. The inference code requires a custom preprocessing step that is not in the default scikit-learn container. What is the simplest way to deploy?

Accepted Answer

Create a custom Docker image extending the SageMaker scikit-learn container. Option C is correct: extending the SageMaker scikit-learn container with a Dockerfile is the simplest. Option A (Lambda) may have compatibility issues. Option B (SageMaker Batch Transform) is for batch, not real-time. Option D (SageMaker Neo) optimizes for hardware, not custom code.

Answer

Package the code in a Lambda layer and use SageMaker hosting

Answer

Use SageMaker Batch Transform with a custom processing script

Answer

Use SageMaker Neo to compile the model and add preprocessing

Question 3

A data scientist needs to store and version machine learning models, along with metadata such as hyperparameters and metrics. Which AWS service is designed for this purpose?

Accepted Answer

Amazon SageMaker Model Registry. Option D is correct: SageMaker Model Registry is for cataloging and versioning models. Option A (S3) is object storage but not specialized. Option B (ECR) stores container images. Option C (DynamoDB) is a NoSQL database, not purpose-built for ML models.

Answer

Amazon S3 with versioning enabled

Answer

Amazon DynamoDB

Answer

Amazon Elastic Container Registry (ECR)

Question 4

A company is using SageMaker to train a linear learner algorithm. The training log shows that the algorithm converges but the final loss is still high. Which change is most likely to improve the model?

Accepted Answer

Add feature crosses or polynomial features. Option B is correct: feature engineering can help linear models capture non-linear patterns. Option A (increasing instances) may not help if model is underfitting. Option C (early stopping) would stop prematurely. Option D (max runtime) does not affect model quality.

Answer

Reduce the early stopping tolerance

Answer

Increase the maximum runtime

Answer

Increase the number of training instances

Question 5

A company wants to automate the retraining of a model weekly using new data. The training script is in a SageMaker notebook. Which implementation is most maintainable?

Accepted Answer

Convert the notebook to a Python script, create a Docker container, and use SageMaker Pipelines with a schedule. Option C is correct: convert the notebook to a Python script, package it in a Docker container, and schedule SageMaker Pipeline runs. Option A (run notebook via Lifecycle Config) is brittle. Option B (IAC) is for infrastructure, not training. Option D (Cron job on EC2) is less managed.

Answer

Set up a cron job on an EC2 instance to run the training script

Answer

Schedule the notebook to run via a SageMaker Lifecycle Configuration script

Answer

Use AWS CloudFormation to provision a training job on a schedule

Question 6

A data scientist needs to select a model training infrastructure that supports distributed training across multiple GPUs and provides automatic model parallelism. Which TWO AWS services should the scientist consider?

Accepted Answer

Amazon EMR. Options A (SageMaker) and B (Amazon EMR) are correct. SageMaker supports distributed training with model parallelism. EMR with Spark supports distributed ML. Option C (AWS Glue) is for ETL, not training. Option D (Amazon Redshift) is a data warehouse. Option E (AWS Lambda) is not for large-scale training.

Answer

AWS Glue

Answer

AWS Lambda

Answer

Amazon Redshift

Question 7

An ML team is deploying a model for real-time inference. They require A/B testing to compare a new model against the existing one. Which THREE steps should they take to set up this test?

Accepted Answer

Set up a second production variant for the existing model. Options A, B, and D are correct. A: Create a production variant with the new model. B: Set up a production variant for the existing model. D: Configure the endpoint to route traffic between variants. Option C (CloudWatch) is monitoring, not part of setup. Option E (Batch Transform) is for batch, not real-time.

Answer

Set up a SageMaker Batch Transform job for each model

Answer

Configure CloudWatch alarms to trigger variant switching

Question 8

A company is using SageMaker to train a model and wants to ensure that the training data is encrypted at rest and in transit, and that the trained model artifacts are also encrypted. Which THREE actions should the company take?

Accepted Answer

Specify a KMS key in the SageMaker training job configuration to encrypt the ML storage volume. Options A, B, and D are correct. A: Enable S3 encryption for the data bucket. B: Use a KMS key for SageMaker training volumes. D: Enable SageMaker model encryption. Option C (CloudTrail) is for auditing, not encryption. Option E (VPC) is for network isolation, not encryption.

Answer

Configure the training job to run in a VPC with no internet access

Answer

Enable AWS CloudTrail to log all API calls

Question 9

A data scientist has this IAM policy attached to their IAM role. They are trying to run a SageMaker training job that reads data from 'my-bucket' and writes output to 'my-bucket'. The job fails. What is the most likely reason?

Accepted Answer

Missing iam:PassRole permission. Option B is correct: the policy does not grant permission to pass the execution role to SageMaker (iam:PassRole). Option A is incorrect because s3:GetObject and s3:PutObject are present. Option C is incorrect because the actions are allowed. Option D is irrelevant.

Answer

The sagemaker:CreateTrainingJob action is not allowed on specific resources

Answer

Missing s3:ListBucket permission on the bucket

Answer

The training job requires permissions to write to CloudWatch Logs

Question 10

A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

Accepted Answer

The training process is generating large temporary files that fill the instance's local storage. Option C is correct: the instance's local storage is full due to temporary files or checkpoints. Option A (insufficient memory) would show MemoryError. Option B (S3 permissions) would show AccessDenied. Option D (data download) would show download errors.

Answer

The training data is not fully downloaded from S3 before processing

Answer

The S3 bucket does not have write permissions

Answer

The training instance does not have enough RAM

Question 11

A company is using Amazon SageMaker to train and deploy a fraud detection model. The model is a gradient boosting machine (GBM) trained on a dataset with 10 million rows and 50 features. The training job runs on an ml.m5.2xlarge instance with 8 vCPUs and 32 GB memory. The training completes successfully, and the model is deployed to a real-time endpoint. After deployment, the inference latency is around 200 ms per request, which is acceptable. However, after a week, the company observes that latency increases to over 1 second during peak hours (12:00-13:00 UTC). CloudWatch metrics show CPU utilization on the endpoint instance reaches 95% during these peaks. The endpoint is configured with a single ml.m5.large instance. The company wants to maintain latency under 500 ms during peak hours without incurring unnecessary cost during off-peak hours. Which solution should the company implement?

Accepted Answer

Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1. Option A is correct: configuring auto scaling based on CPU utilization adds instances during peak and removes them during off-peak, meeting latency and cost goals. Option B (Batch Transform) is for offline inference. Option C (larger instance) is less cost-effective. Option D (scale down) would worsen latency.

Answer

Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00

Answer

Switch to SageMaker Batch Transform and have the application send requests in batches

Answer

Replace the endpoint instance type with ml.m5.4xlarge to handle peak load

Question 12

A data scientist is training a TensorFlow model on a single GPU instance. The training is taking too long. Which AWS service should be used to reduce training time by distributing the workload across multiple GPUs?

Accepted Answer

Amazon SageMaker. Amazon SageMaker supports distributed training across multiple GPUs using the SageMaker distributed training libraries. Option B is correct. Option A is wrong because AWS Batch is for batch computing, not specifically optimized for GPU training. Option C is wrong because Amazon EMR is for big data processing. Option D is wrong because AWS Glue is for ETL jobs.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Batch

Question 13

A company deploys a machine learning model on Amazon SageMaker for real-time inference. The model receives requests with large payloads (up to 5 MB) and the inference latency is high. Which configuration change would MOST likely reduce latency?

Accepted Answer

Use a larger instance type with more memory and compute. Using multi-model endpoints reduces latency by loading only the required model into memory, but for large payloads, increasing instance size (Option B) helps handle compute and memory needs. Option A is wrong because SageMaker does not support payload compression natively. Option C is wrong because reducing batch size increases latency. Option D is wrong because containers are automatically loaded; pre-loading is not an option.

Answer

Pre-load multiple model containers on the same endpoint

Answer

Reduce the batch size for inference requests

Answer

Enable payload compression using SageMaker built-in compression

Question 14

A machine learning team is using Amazon SageMaker to train a model with a custom algorithm packaged in a Docker container. The training job fails with the error 'Error: Unable to locate sagemaker-training toolkit.' What is the MOST likely cause?

Accepted Answer

The container does not include the SageMaker Training Toolkit. SageMaker requires the container to include the SageMaker Training Toolkit (or the common library) for integration. Option B is correct. Option A is wrong because the error is about the toolkit, not network. Option C is wrong because the error is clear about missing toolkit. Option D is wrong because the container is custom, not the default.

Answer

The container does not have internet access to download dependencies

Answer

The instance type is incompatible with the container

Answer

The training role does not have permissions to access the container repository

Question 15

A data scientist needs to perform hyperparameter optimization for a model. Which AWS service provides built-in hyperparameter tuning jobs?

Accepted Answer

Amazon SageMaker. Amazon SageMaker provides built-in hyperparameter tuning (automatic model tuning). Option C is correct. Option A is wrong because AWS Batch is not for tuning. Option B is wrong because Amazon EMR is for big data. Option D is wrong because AWS Step Functions orchestrates workflows but does not perform tuning.

Answer

Amazon EMR

Answer

AWS Step Functions

Answer

AWS Batch