MLS-C01 Machine Learning Implementation and Operations Practice Test 22 — 15 Questions

Question 1

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 (A) is correct because SageMaker automatically publishes model and endpoint metrics such as Invocations, ModelLatency, and 4XX/5XX errors to CloudWatch, and you can create alarms and dashboards to detect performance degradation in production. Amazon SageMaker Model Monitor (D) is correct because it is the purpose-built service that continuously monitors deployed endpoints for data drift, model quality drift, bias drift, and feature attribution drift, emitting violations to CloudWatch and Amazon S3. AWS X-Ray (B) is not correct here because it traces requests through distributed applications for latency and error debugging, not model performance or drift monitoring. Amazon Inspector (C) is not correct because it is a vulnerability management service for EC2, container images, and Lambda, not ML model monitoring. AWS Config (E) is not correct because it records and evaluates resource configuration changes for compliance, not model inference performance.

Answer

AWS X-Ray

Answer

Amazon Inspector

Answer

AWS Config

Question 2

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 3

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 means the model is underfitting — it is too simple to capture the underlying relationship in the data. Adding polynomial features increases the model's capacity to represent non-linear relationships, which directly reduces bias. Linear regression on 10 raw features may be too rigid if the true relationship is curved.

Answer

Increase the regularization parameter lambda

Answer

Add L2 regularization

Answer

Use a smaller training dataset

Question 4

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. Option B is correct because a CannotPullContainerError commonly occurs when the specified image tag does not exist in the ECR repository, so confirming the tag exists ensures SageMaker can reference a valid image. Option C is correct because the SageMaker training job's execution role must have the necessary IAM permissions, such as ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, and ecr:BatchCheckLayerAvailability, to pull the image from ECR. Option A is not appropriate because switching to a public image would abandon the custom training environment rather than fix the ECR pull issue. Option D is incorrect because a timeout increase does not address an image pull failure. Option E is incorrect because the training instance does not need general internet access to pull from ECR when the required VPC endpoints or NAT configuration and IAM permissions are properly set.

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 5

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. Option B is correct because SageMaker Managed Spot Training relies on EC2 Spot capacity, which can be reclaimed with a two-minute interruption notice, so the training job must tolerate being stopped and resumed rather than failing outright. Option C is correct because spot instances are only offered for certain instance types and in certain Availability Zones; if the selected instance type has no spot capacity in the region/AZ, the managed spot training job cannot launch on spot. Option D is correct because SageMaker Managed Spot Training resumes a job from the last saved checkpoint after an interruption, so the training script must periodically write checkpoints (typically to Amazon S3) to avoid restarting from scratch. Option A is not required because serverless endpoints are an inference deployment feature and are unrelated to whether a training job uses spot instances. Option E is not required because running the training job inside a VPC is a networking choice for private resource access, not a prerequisite for Managed Spot Training.

Answer

The model must be deployed to a serverless endpoint

Answer

The training job must be configured to run in a VPC

Question 6

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. Option C is correct because encrypting the raw data at rest with S3 server-side encryption (SSE-S3, SSE-KMS, or SSE-C) protects the training data from unauthorized access if the underlying storage is compromised, which is a fundamental data-privacy requirement before feeding data into SageMaker. Option E is correct because running SageMaker jobs inside a VPC and using VPC endpoints (AWS PrivateLink) for S3 keeps traffic between the training containers and S3 on the AWS private network, preventing exposure over the public internet and enforcing network-level isolation. Option A is not essential because restricting access to a single IAM role is only one aspect of least-privilege access and does not by itself guarantee privacy/security of the data at rest or in transit; broader IAM and bucket policies are already implied. Option B is not essential because S3 versioning aids durability and recovery from accidental deletion or overwrites, not data privacy or security. Option D is not essential because S3 Transfer Acceleration only improves upload performance via edge locations and has no bearing on privacy or security.

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 7

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 must grant permissions for all actions the training job needs, including writing logs to CloudWatch Logs. If the policy lacks the necessary CloudWatch Logs actions (e.g., logs:CreateLogGroup, logs:CreateLogStream, logs:PutLogEvents), the job will fail when attempting to write logs. The other options are less likely because SageMaker API version is not specified in IAM policies, S3 bucket policy denial would affect S3 access not CloudWatch, and an implicit deny for SageMaker actions would prevent the job from starting at all.

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 8

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. The error in the exhibit indicates that the model package ARN does not exist in the registry. This occurs when the model package version has not been successfully created or registered, meaning it does not exist. Option B is therefore correct: the model package version does not exist. Option A would result in an access denied error, not this ARN-not-found error. Option C is incorrect because an 'Approved' status is actually required for deployment, so it would not cause this error. Option D would produce a different error about an existing endpoint configuration or conflict, not a missing ARN.

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 9

A data scientist needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to create a SageMaker endpoint?

Accepted Answer

SageMaker Model and Endpoint Configuration. To deploy a model to a SageMaker real-time endpoint, you create a SageMaker Model resource (pointing to the model artifacts in S3 and the inference container image), then an Endpoint Configuration (specifying instance type, count, and variants), and finally an Endpoint that references that configuration. This three-step process is the native SageMaker deployment path for real-time inference.

Answer

AWS Lambda

Answer

AWS CloudFormation

Answer

Amazon ECS

Question 10

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. SageMaker Debugger provides built-in profiling that captures system metrics (CPU, GPU, memory, I/O) and framework metrics during training, and can identify data-loading bottlenecks via its profiling reports and rules. It is purpose-built to diagnose training performance issues, including input pipeline stalls. This makes it the most effective diagnostic action.

Answer

Monitor CPU utilization in CloudWatch

Answer

Enable SageMaker Model Monitor

Answer

Increase the instance type to a larger one

Question 11

A company is using Amazon SageMaker to run a hyperparameter tuning job. The tuning job uses Bayesian optimization. Which THREE statements about Bayesian optimization are correct? (Choose THREE.)

Accepted Answer

It works well for continuous hyperparameters. Options B, C, and D are correct. Bayesian optimization uses past trial results to select hyperparameter combinations (C), it works well for continuous hyperparameters (B), and it typically finds optimal values in fewer trials than random search (D). Option A is false because Bayesian optimization can handle many hyperparameters, not just 5. Option E is false because Bayesian optimization generally requires fewer trials than grid search, not more.

Answer

It can only handle a maximum of 5 hyperparameters

Answer

It requires more trials than grid search to find optimal values

Question 12

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 keep compute instances continuously running behind a persistent HTTPS endpoint, so requests are served immediately without cold starts or queueing. This architecture delivers consistent single-digit millisecond latency, which is why it is the standard choice for latency-sensitive, always-on inference. Asynchronous and Serverless options introduce queuing or cold-start overhead that breaks the single-digit millisecond requirement.

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Answer

SageMaker Batch Transform

Question 13

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 in the training script decreases GPU memory usage per iteration, which can resolve the OutOfMemory error without changing the model architecture. Option A is incorrect because switching to a regression model changes the problem type. Option B is incorrect because increasing the number of epochs does not affect memory per step. Option C is incorrect because Managed Warm Pools are for reducing cold start times, not for memory issues.

Answer

Switch to a regression model

Answer

Increase the number of epochs

Answer

Enable SageMaker Managed Warm Pools

Question 14

A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?

Accepted Answer

Enable Managed Spot Training. Managed Spot Training on SageMaker uses spare EC2 capacity at up to 90% discount compared to on-demand pricing, making it the most cost-effective option for a 10-hour training job that can tolerate interruption. SageMaker automatically checkpoints and resumes training if the spot instance is reclaimed, so long jobs remain viable.

Answer

Use SageMaker Automatic Model Tuning

Answer

Use a larger instance type to finish faster

Answer

Use SageMaker Distributed Training with more instances

Question 15

A company deploys a SageMaker endpoint for real-time inference. After a week, the response latency increases from 50 ms to 500 ms. CPU utilization is at 30%. What is the most likely cause?

Accepted Answer

The inference code makes a call to a downstream service that is throttling requests. CPU utilization is only 30%, which rules out compute saturation, and the latency degraded gradually over a week rather than abruptly — consistent with a downstream dependency (e.g., a feature store, database, or third-party API) beginning to throttle requests. The inference container waits on the throttled call, inflating end-to-end latency while its own CPU stays idle.

Answer

The model has a memory leak

Answer

The instance type is underpowered for the inference load

Answer

The SageMaker endpoint is experiencing a network outage