MLS-C01 Machine Learning Implementation and Operations Practice Test 6 — 15 Questions

Question 1

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. Which action will the notebook be able to perform?

Accepted Answer

Invoke a SageMaker endpoint. The IAM policy attached to the SageMaker notebook instance grants only the `sagemaker:InvokeEndpoint` action. This action allows the notebook to send inference requests to a deployed SageMaker endpoint. No other SageMaker or S3 actions are permitted, so the notebook can only invoke the endpoint.

Answer

Create a training job

Answer

Create a model

Answer

Read data from S3

Question 2

Refer to the exhibit. A SageMaker endpoint logs this error. What is the most likely cause?

Accepted Answer

The input data has fewer features than the model expects. The error log indicates a mismatch between the number of features in the input data and the number of features the model was trained on. SageMaker's inference endpoint validates the input shape against the model's expected feature dimensions; when the input has fewer features, the model cannot perform the matrix operations required for prediction, resulting in this error. Option D correctly identifies this feature count mismatch as the root cause.

Answer

The model is corrupted

Answer

There is a network connectivity issue

Answer

The input data type is incorrect

Question 3

A machine learning team is deploying a model using Amazon SageMaker. The model inference code runs on GPUs and requires a custom container. The team wants to minimize cold start latency. Which SageMaker hosting option should they use?

Accepted Answer

Use a real-time endpoint with a single production variant using a GPU instance.. A real-time endpoint with a single production variant using a GPU instance provides a persistent, always-on inference environment. This eliminates cold start latency entirely, as the container and GPU resources are pre-warmed and ready to serve requests immediately, which is critical for minimizing latency in GPU-based custom containers.

Answer

Use a multi-model endpoint with GPU instances.

Answer

Use a serverless inference endpoint.

Answer

Use a real-time endpoint with multiple production variants for redundancy.

Question 4

A data scientist is training a deep learning model on a large dataset using SageMaker. The training job is taking too long. Upon reviewing the CloudWatch logs, the scientist notices that the GPU utilization is below 10% most of the time. Which change is MOST likely to improve GPU utilization and reduce training time?

Accepted Answer

Increase the batch size in the training script.. Low GPU utilization (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total training time.

Answer

Use a different optimizer that requires less computation.

Answer

Switch to a smaller instance type to reduce data transfer overhead.

Answer

Reduce the size of the training dataset.

Question 5

A company is using Amazon SageMaker to train a model and wants to track hyperparameter tuning jobs. Which AWS service is BEST suited to store and query metadata such as tuning job configurations and results?

Accepted Answer

Amazon SageMaker Experiments. Amazon SageMaker Experiments is purpose-built for tracking, organizing, and querying metadata from machine learning training runs, including hyperparameter tuning jobs. It automatically captures configurations, metrics, and results, and provides a Python SDK and SDK API to search and compare trials, making it the best choice for this use case.

Answer

Amazon CloudWatch Logs

Answer

Amazon S3 with Amazon Athena

Answer

Amazon DynamoDB

Question 6

A machine learning engineer needs to deploy a model that performs real-time inference with strict latency requirements of under 100 milliseconds. The model is a large ensemble of 10 deep learning models. Which SageMaker deployment strategy is MOST appropriate?

Accepted Answer

Use a SageMaker Inference Pipeline with serial inference within a single endpoint.. A SageMaker Inference Pipeline allows you to chain multiple containers (e.g., the 10 deep learning models) within a single endpoint, enabling serial inference with low latency. This approach avoids the network overhead of routing between separate endpoints and keeps the entire ensemble under the 100 ms threshold by processing sequentially in one HTTPS request.

Answer

Use batch transform and cache predictions.

Answer

Deploy each model as a separate endpoint and route traffic using Application Load Balancer.

Answer

Use a multi-model endpoint to host all models.

Question 7

A data scientist is using SageMaker Autopilot to automatically build a classification model. The dataset is highly imbalanced (1% positive class). Which configuration should the scientist set to handle the class imbalance?

Accepted Answer

Use the 'AutoMLJobObjective' with 'F1' metric.. SageMaker Autopilot does not support direct class weighting or sample weights for imbalanced datasets. By setting the objective metric to 'F1', Autopilot will optimize the model for the harmonic mean of precision and recall, which is more robust to class imbalance than accuracy. This encourages the model to pay attention to the minority (positive) class during training and hyperparameter tuning.

Answer

Set the problem_type to 'BinaryClassification' and enable 'balance_class_weights'.

Answer

Set the 'sample_weight' attribute in the input data.

Answer

Manually downsample the majority class before training.

Question 8

A company is using Amazon SageMaker to train a model and wants to automatically retrain the model every week using new data. Which AWS service should be used to orchestrate the retraining pipeline?

Accepted Answer

AWS Step Functions. AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multiple AWS services (e.g., SageMaker training jobs, Lambda functions, and data processing) into a state machine. It supports scheduling via Amazon EventBridge (formerly CloudWatch Events) to trigger the pipeline weekly, and it can handle retries, error handling, and parallel execution, making it ideal for automating a retraining pipeline.

Answer

Amazon CloudWatch Events

Answer

AWS Lambda

Answer

AWS Data Pipeline

Question 9

A machine learning team is using SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The training job fails with an 'AccessDenied' error. Which IAM permission is MOST likely missing from the SageMaker execution role?

Accepted Answer

kms:Decrypt. The training data is stored in an S3 bucket encrypted with AWS KMS. When SageMaker reads the encrypted data, the execution role must have permission to decrypt the KMS key. Without `kms:Decrypt`, the role cannot access the encrypted objects, resulting in an 'AccessDenied' error even if S3 read permissions are present.

Answer

s3:GetObject

Answer

s3:ListBucket

Answer

kms:GenerateDataKey

Question 10

A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The labeling job is taking longer than expected. The team notices that many workers are spending a lot of time on images with no objects. Which labeling strategy should they use to reduce costs and time?

Accepted Answer

Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.. It introduces a two-stage labeling workflow: first, workers perform a quick binary classification to identify images containing objects, and only those positive images proceed to the expensive, time-consuming bounding box annotation. This directly reduces the cost and time spent on empty images, which is the root cause of the delay.

Answer

Use a private workforce instead of public.

Answer

Use automated data labeling with a pre-trained model to filter empty images.

Answer

Increase the number of workers per dataset object.

Question 11

A data scientist needs to run a one-time training job on a large dataset using SageMaker. The job requires a specific PyTorch version and custom dependencies. Which approach is MOST efficient?

Accepted Answer

Use the SageMaker PyTorch estimator with a pre-built container.. The SageMaker PyTorch estimator provides a pre-built, optimized container with the specified PyTorch version, eliminating the need to manage custom Docker images or manual dependency installation. For a one-time training job, this approach is the most efficient as it requires minimal setup and leverages SageMaker's managed infrastructure for training.

Answer

Create a custom Docker container and push to ECR.

Answer

Launch a SageMaker notebook instance, install dependencies, and run training script.

Answer

Use the SageMaker generic container and install PyTorch via a lifecycle configuration.

Question 12

Which TWO factors should be considered when choosing between Amazon SageMaker's real-time endpoints and serverless inference? (Select TWO.)

Accepted Answer

GPU requirement. GPU requirement is a key factor because SageMaker real-time endpoints support GPU-based instances (e.g., ml.p3, ml.g4dn) for low-latency inference on deep learning models, while serverless inference only supports CPU instances. If your model requires GPU acceleration for acceptable latency, you must choose a real-time endpoint.

Answer

Integration with AWS Lambda

Answer

Availability of built-in algorithms

Answer

Model size in GB

Question 13

Which THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select THREE.)

Accepted Answer

Enable SageMaker Neo to compile the model.. A is correct because SageMaker Neo compiles the trained model into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia), using Apache TVM to fuse operations and prune unused computations. This reduces inference latency by up to 2x without requiring code changes, making it a direct latency-reduction measure for real-time endpoints.

Answer

Increase the batch size for inference.

Answer

Use a multi-model endpoint to share the instance.

Question 14

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Accepted Answer

Disable direct internet access for the notebook instance.. Disabling direct internet access for a SageMaker notebook instance (Option A) is a best practice because it prevents the instance from reaching the public internet, reducing the attack surface. This forces all outbound traffic through a VPC, allowing you to control egress via NAT gateways or VPC endpoints, and ensures data does not traverse the public internet. It is a fundamental security hardening step for sensitive workloads.

Answer

Enable root access for users to install packages.

Answer

Store data in the notebook's local storage for performance.

Answer

Use a shared IAM user for all data scientists.

Question 15

A data scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?

Accepted Answer

Reduce the batch size in the training script.. An OutOfMemoryError during GPU training indicates that the GPU's memory is exhausted. Reducing the batch size directly decreases the memory footprint per training step, as fewer samples and their corresponding activations are stored simultaneously. This is the most immediate and effective fix without changing the instance type or training architecture.

Answer

Enable automatic hyperparameter tuning.

Answer

Switch to distributed training across multiple instances.

Answer

Use a smaller instance type with less GPU memory.