MLS-C01 Practice Exam 65 — 20 Questions

Question 1

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 B and E are correct because encryption at rest and in transit are essential. Option A is optional. Option C is for access control, but encryption is more fundamental. Option D is 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 2

A data scientist wants to deploy a PyTorch model for real-time inference. Which SageMaker deployment option provides the lowest latency for single-digit millisecond responses?

Accepted Answer

SageMaker Real-Time Inference endpoint. SageMaker Real-Time Inference endpoints are designed for low-latency, real-time predictions. Option B is correct because SageMaker Serverless Inference can have cold starts and higher latency. Option C is for batch processing. Option D is for asynchronous inference with higher latency.

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Answer

SageMaker Batch Transform

Question 3

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. Option B is correct because the policy does not include CloudWatch Logs permissions. Option A is incorrect because the actions are allowed for all resources. Option C is incorrect because the S3 actions are allowed. Option D is incorrect because there is no deny statement.

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 4

A team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?

Accepted Answer

Reduce the batch size in the training script. Reducing the batch size decreases GPU memory usage per iteration. Option A is correct. Option B changes the problem to regression. Option C increases memory usage. Option D is unrelated to GPU memory.

Answer

Switch to a regression model

Answer

Increase the number of epochs

Answer

Enable SageMaker Managed Warm Pools

Question 5

A machine learning team is using SageMaker Pipelines to orchestrate a multi-step workflow. The pipeline fails with a 'ThrottlingException' when submitting a training job. Which TWO actions can reduce the likelihood of throttling?

Accepted Answer

Implement retry logic with exponential backoff in the pipeline. Throttling occurs due to API rate limits. Implementing exponential backoff and reducing concurrent submissions help. Options C and D are correct. Option A increases load, Option B is for model registry, Option E is not possible.

Answer

Use SageMaker Model Registry to version models

Answer

Increase the number of parallel training jobs

Answer

Request a service quota increase for training jobs

Question 6

An engineer sees the error in the exhibit when trying to deploy a model from a model registry in SageMaker. What is the MOST likely cause?

Accepted Answer

The model package version does not exist in the registry. Option A is correct because the error indicates the model package ARN does not exist. Option B would show a different error. Option C is not indicated. Option D would show a different error.

Answer

The IAM role lacks permission to access the model registry

Answer

The model package is still in 'Approved' status

Answer

The SageMaker endpoint is already deployed with the same model

Question 7

A machine learning engineer needs to store and version datasets for reproducibility. Which AWS service is designed for this purpose?

Accepted Answer

SageMaker Feature Store. SageMaker Feature Store stores, manages, and versions features for ML. Option B is correct. Option A is a general object store. Option C is for code versioning. Option D is for data warehousing.

Answer

AWS CodeCommit

Answer

Amazon S3

Answer

Amazon Redshift

Question 8

A machine learning team is deploying a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (<100 ms). The model is a PyTorch model with custom pre- and post-processing logic. The team uses a SageMaker Model with a custom inference container. After deployment, they observe that the endpoint takes over 500 ms for the first request, but subsequent requests are fast (~50 ms). What is the MOST likely cause?

Accepted Answer

The container has a cold start delay because the model needs to be loaded into memory from Amazon S3 on the first request.. Option B is correct because the first request often suffers from cold start latency due to container initialization and model loading. Option A is wrong because the issue is transient and not related to instance type. Option C is wrong because the model is large but cold start is the primary cause. Option D is wrong because the issue is not about auto-scaling but about initialization.

Answer

The instance type is too small to handle the model size.

Answer

The model is too large and exceeds the instance memory.

Answer

The endpoint is not configured with auto-scaling.

Question 9

A machine learning team is using Amazon SageMaker to train a model. The training job uses spot instances to reduce cost. However, the training job is frequently interrupted. Which TWO actions can help mitigate the impact of spot interruptions? (Choose TWO.)

Accepted Answer

Use managed spot training with SageMaker's 'ManagedSpotTraining' parameter set to True.. Options B and D are correct. Option B: Using checkpoints allows resuming from the last saved state. Option D: Using managed spot training automatically handles interruptions. Option A is wrong because more instances increase cost and interruption risk. Option C is wrong because larger instances are more expensive and not guaranteed to be less interrupted. Option E is wrong because on-demand instances cost more.

Answer

Increase the number of training instances.

Answer

Use a larger instance type that is less likely to be interrupted.

Answer

Switch to on-demand instances.

Question 10

A company uses Amazon SageMaker to train machine learning models. The training data contains personally identifiable information (PII). The company needs to ensure that the data is encrypted in transit between S3 and SageMaker. Which configuration is REQUIRED?

Accepted Answer

Use SageMaker in a VPC with VPC endpoints for S3. Option A is correct because SageMaker uses HTTPS by default for S3 access. Option B is for data at rest. Option C is optional. Option D is not required for encryption in transit.

Answer

Enable S3 server-side encryption

Answer

Set the S3 bucket policy to require SSL

Answer

Use a custom Docker image with TLS configured

Question 11

A model deployed on a SageMaker endpoint is producing predictions that are consistently biased against a certain demographic. Which step should the team take FIRST to address this issue?

Accepted Answer

Use SageMaker Clarify to analyze bias in the training data and predictions. The first step is to analyze the data and model for bias. SageMaker Clarify can detect bias. Option B is correct. Option A is a solution after analysis. Option C changes model. Option D is a general practice.

Answer

Enable SageMaker Model Monitor to track prediction quality

Answer

Switch to a different algorithm that is less prone to bias

Answer

Retrain the model with balanced data

Question 12

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model requires GPU for inference. Which instance type should be selected?

Accepted Answer

ml.p3.2xlarge. Option D is correct because P3 instances (e.g., ml.p3.2xlarge) provide GPU capabilities. Options A, B, and C are CPU-only instances.

Answer

ml.m5.large

Answer

ml.c5.xlarge

Answer

ml.r5.large

Question 13

A machine learning engineer is deploying a model using SageMaker and wants to use automatic scaling for the endpoint based on the number of concurrent requests. The engineer has defined a scaling policy using the SageMakerVariantInvocationsPerInstance metric. However, the scaling is not triggering as expected. What could be the issue?

Accepted Answer

The scaling policy does not have a cooldown period configured, or the cooldown period is too long.. Option D is correct because scaling policies require a cooldown period (default 300 seconds) to prevent rapid scaling. Without it, the policy may not activate. Option A is wrong because the metric is valid. Option B is wrong because the metric is emitted by default. Option C is wrong because scaling policy can be defined without a scheduled action.

Answer

A scheduled scaling action must be created first.

Answer

The metric must be published to CloudWatch manually.

Answer

The metric is not available for automatic scaling.

Question 14

A team is training an XGBoost model using SageMaker with a large dataset in S3 (100 GB). Training is taking too long. Which change will most likely reduce training time without sacrificing accuracy?

Accepted Answer

Configure Pipe mode for data input. Option D is correct: enabling SageMaker Managed Spot Training reduces cost but does not accelerate training. Option A (increasing instance count) can reduce training time via distributed training. Option B (using Pipe mode) streams data for faster I/O. Option C (reducing instance count) would increase time. Option D is about cost, not speed.

Answer

Reduce the number of training instances

Answer

Enable SageMaker Managed Spot Training

Answer

Use a larger instance type with more vCPUs

Question 15

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 16

A company wants to track and compare metrics from multiple machine learning experiments. Which Amazon SageMaker feature should be used?

Accepted Answer

SageMaker Experiments. SageMaker Experiments helps track and compare metrics. Option B is correct. Option A is wrong because Model Monitor is for monitoring drift. Option C is wrong because Debugger is for debugging training. Option D is wrong because Ground Truth is for labeling.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Question 17

A machine learning engineer is using Amazon SageMaker to train a model. The training job is taking too long. The engineer suspects the data loading is a bottleneck. Which action would MOST effectively diagnose the issue?

Accepted Answer

Use SageMaker Debugger to profile the training job. Option C is correct because SageMaker Debugger can profile the training job and identify bottlenecks. Option A may add overhead. Option B is not detailed. Option D is for inference.

Answer

Monitor CPU utilization in CloudWatch

Answer

Enable SageMaker Model Monitor

Answer

Increase the instance type to a larger one

Question 18

A machine learning team is using SageMaker to train a model with a custom Docker container. The training script runs locally but fails on SageMaker with a 'Permission denied' error when writing to /opt/ml/model. What is the likely cause?

Accepted Answer

The container's user does not have write permission to /opt/ml/model. SageMaker expects the container to write the model artifact to /opt/ml/model. If the user in the container lacks write permissions, it fails. Option C is correct. Option A is unrelated. Option B would cause different errors. Option D is about reading data.

Answer

The Docker image is too large

Answer

The training script is trying to read from /opt/ml/input/data instead of /opt/ml/input/data/training

Answer

The training data is not in the correct S3 bucket

Question 19

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team has limited budget and wants to minimize labeling costs while ensuring high-quality labels. Which approach is MOST cost-effective?

Accepted Answer

Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.. Option D is correct because active learning selects the most uncertain samples for human labeling, reducing the number of labels needed while maintaining quality. Option A is wrong because using only automated labeling may introduce errors. Option B is wrong because using only workforce is expensive. Option C is wrong because using all workers is costly.

Answer

Use only a private workforce of domain experts to label all data.

Answer

Use a public workforce and have each data point labeled by three workers.

Answer

Use the built-in automated labeling feature without human review.

Question 20

A data scientist is using SageMaker Ground Truth to create a labeled dataset for object detection. After the labeling job completes, the scientist notices that the output manifest file contains incorrect labels. What is the most efficient way to correct these labels?

Accepted Answer

Create an incremental labeling job that includes only the mislabeled items.. Option B is correct because Ground Truth allows creating an incremental labeling job to correct only the mislabeled items, avoiding re-labeling all data. Option A is wrong because it would require re-labeling everything, which is inefficient. Option C is wrong because it does not address labeling errors. Option D is wrong because it would lose the correctly labeled data.

Answer

Delete the labeling job and start over with a different set of workers.

Answer

Use the SageMaker console to edit the incorrect labels directly in the manifest file.

Answer

Create a new labeling job with the same dataset and manually verify all labels.