MLS-C01 Machine Learning Implementation and Operations Practice Test 13 — 15 Questions

Question 1

A research lab is using SageMaker to train deep learning models on a custom dataset stored in S3. Each training job uses a single ml.p3.2xlarge instance. Recently, training jobs have been failing intermittently with 'NetworkError: Connection reset by peer' during the data download phase. The data scientist notices that the dataset is 50GB and the network throughput is low. The training script uses the default S3 download method (boto3) to copy data from S3 to the local instance storage. Which solution should the data scientist implement to resolve the issue?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3.. Option D is correct because using SageMaker's Pipe mode streams data directly from S3 without writing to local disk, which is more reliable and avoids large downloads. Option A is incorrect because simply retrying may not fix the underlying network issue. Option B is incorrect because using a larger instance does not guarantee improved network reliability. Option C is incorrect because using EBS volumes adds cost and does not solve the network reset issue; data still needs to be downloaded.

Answer

Mount an EBS volume to the instance and copy data there before training.

Answer

Add retry logic in the training script to handle network errors.

Answer

Use a larger instance type like p3.8xlarge for better network bandwidth.

Question 2

A media company uses SageMaker to train a recommendation model. The training data is stored in an S3 bucket with versioning enabled. The data pipeline updates the training data daily by overwriting objects with new data. Recently, the model's performance degraded, and the team suspects that the training data was corrupted on a specific day. They want to train the model using the data from a previous version. How can the team retrieve the previous version of the training data?

Accepted Answer

Use the S3 GET Object Version API to download the specific version of each object.. Option A is correct because S3 versioning allows retrieving any previous version of an object by specifying the version ID. The team can list versions, identify the correct version, and use it for training. Option B is incorrect because S3 Select is for querying data within an object, not for version retrieval. Option C is incorrect because S3 Glacier is for archival, not for accessing previous versions of current objects. Option D is incorrect because S3 replication does not help in retrieving previous versions from the same bucket.

Answer

Restore the bucket from S3 Glacier, which contains the previous version.

Answer

Use S3 Select to query the previous version of the data.

Answer

Enable S3 replication to a different bucket and use the replicated data.

Question 3

A data scientist is deploying a machine learning model on Amazon SageMaker for real-time inference. The model requires low-latency predictions and must be able to handle up to 1000 requests per second. Which TWO actions should the data scientist take to ensure the endpoint can meet the performance requirements? (Choose 2.)

Accepted Answer

Enable data capture to Amazon S3 for model monitoring and retraining.. Option B is correct because enabling data capture to S3 allows model monitoring and retraining. Option D is correct because auto scaling adjusts instances based on load. Option A is wrong because serverless inference has a cold start and max concurrency limits unsuitable for 1000 TPS. Option C is wrong because increasing instance size alone may not be cost-effective and auto scaling is better. Option E is wrong because multi-model endpoints share resources and may cause contention.

Answer

Use a multi-model endpoint to host multiple models on the same instance.

Answer

Use a serverless inference endpoint to automatically scale.

Answer

Deploy the model on a single large instance (e.g., ml.p3.16xlarge).

Question 4

A machine learning team is using Amazon SageMaker to train a deep learning model on a large dataset stored in Amazon S3. The training job is taking too long. The team wants to reduce training time without modifying the model architecture. Which THREE actions should the team take? (Choose 3.)

Accepted Answer

Enable SageMaker Managed Spot Training to use cheaper spot instances.. Option B is correct because Pipe mode streams data directly from S3, reducing download time. Option C is correct because SageMaker Managed Spot Training can reduce training time by using cheaper spot instances. Option D is correct because distributed training with multiple GPUs can parallelize computation. Option A is wrong because increasing instance size may help but is less effective than distributed training. Option E is wrong because SageMaker Processing is for data processing, not training.

Answer

Use a larger instance type with more vCPUs and memory.

Answer

Use SageMaker Processing to preprocess the data.

Question 5

A machine learning engineer is deploying a custom XGBoost model for real-time inference on Amazon SageMaker. The model was trained using the SageMaker XGBoost built-in algorithm. The endpoint is deployed with an ml.m5.large instance and is receiving around 50 requests per second. The engineer notices that the endpoint's latency is around 200 ms, but the requirement is under 100 ms. The model's serialized format is a .tar.gz file. The engineer wants to reduce inference latency without modifying the model or retraining. What should the engineer do?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance.. Option C is correct because SageMaker Neo optimizes trained models for target hardware, reducing latency. Option A is wrong because SageMaker Batch Transform is for batch, not real-time. Option B is wrong because SageMaker Debugger monitors training, not inference. Option D is wrong because Elastic Inference attaches GPU acceleration for deep learning, not XGBoost which is tree-based.

Answer

Configure SageMaker Debugger to optimize the inference code.

Answer

Use SageMaker Elastic Inference to attach an accelerator.

Answer

Use SageMaker Batch Transform instead of a real-time endpoint.

Question 6

A machine learning team is using Amazon SageMaker to train a PyTorch model on a dataset that is 500 GB in size. The training job runs on a single ml.p3.2xlarge instance, but the training takes over 48 hours, which exceeds the maximum allowed time. The team wants to reduce training time to under 24 hours. They are open to using multiple instances and have budget for up to 4 instances. The dataset is stored in Amazon S3 and can be split into shards by a key. The model architecture must remain unchanged. What should the team do?

Accepted Answer

Use SageMaker distributed data parallelism with 4 ml.p3.2xlarge instances.. Option D is correct because SageMaker's distributed data parallelism library (SMDDP) can efficiently split data across multiple GPUs with minimal code changes. Option A is wrong because increasing instance type alone may not halve training time. Option B is wrong because Pipe mode reduces I/O but not computation time. Option C is wrong because SageMaker Processing is for preprocessing, not training.

Answer

Use SageMaker Processing to split the data and train separate models.

Answer

Change the instance type to ml.p3.16xlarge.

Answer

Switch to Pipe input mode to stream data faster.

Question 7

A company is using Amazon SageMaker to host a model for real-time inference. The model was trained using SageMaker's built-in Linear Learner algorithm. The endpoint has been running for a week, and the operations team notices that the endpoint's latency has increased from 50 ms to 150 ms over the past few days. The number of requests per second has remained steady at about 200. The team suspects a memory leak in the inference container. What should the team do to diagnose the issue?

Accepted Answer

Enable CloudWatch Logs and use Container Insights to view memory utilization.. Option B is correct because CloudWatch Logs and container insights can show memory usage over time. Option A is wrong because CloudWatch metrics show latency but not memory directly. Option C is wrong because SageMaker Debugger is for training. Option D is wrong because SageMaker Model Monitor is for data drift, not memory leaks.

Answer

Use Amazon CloudWatch to monitor the endpoint's latency metric.

Answer

Use SageMaker Debugger to inspect the inference container.

Answer

Use SageMaker Model Monitor to detect data drift.

Question 8

A data scientist is using Amazon SageMaker to train a TensorFlow model on a dataset that includes sensitive personal information (PII). The data is stored in Amazon S3 with server-side encryption using AWS KMS (SSE-KMS). The training job fails with an Access Denied error when trying to read from S3. The data scientist has already verified that the SageMaker execution role has s3:GetObject permissions on the S3 bucket. What additional configuration is needed?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role.. Option A is correct because SageMaker needs kms:Decrypt permission to read SSE-KMS encrypted objects. Option B is wrong because SageMaker does not need kms:Encrypt for reading. Option C is wrong because S3 bucket policy is not needed if role has permissions. Option D is wrong because VPC endpoint policy is not the issue.

Answer

Add kms:Encrypt permission to the SageMaker execution role.

Answer

Add a bucket policy that grants s3:GetObject to the SageMaker role.

Answer

Configure a VPC endpoint for S3 and attach a policy.

Question 9

A company is using Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.c5.xlarge instance. The company wants to reduce costs without affecting performance. The current traffic pattern shows a daily peak of 500 requests per second for 2 hours, and the rest of the day sees fewer than 50 requests per second. The model has a cold start time of about 30 seconds. What should the company do?

Accepted Answer

Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.. Option D is correct because adding a scaling policy to scale down during low traffic reduces cost, and keeping a minimum instance ensures low latency during low traffic without cold starts. Option A is wrong because serverless endpoints have cold starts and may not handle 500 TPS. Option B is wrong because Batch Transform is not for real-time. Option C is wrong because one instance during peak may cause latency.

Answer

Switch to a serverless inference endpoint.

Answer

Use a single ml.c5.xlarge instance and rely on it.

Answer

Use SageMaker Batch Transform for all predictions.

Question 10

A machine learning engineer is deploying a model on Amazon SageMaker that was trained using a custom Docker container. The container is stored in Amazon ECR. The engineer creates a SageMaker model and endpoint configuration, but when creating the endpoint, it fails with an error: 'Could not find the inference code at the expected path.' The engineer verified that the container image is correct and the model artifacts are in S3. What is the most likely cause?

Accepted Answer

The inference code is not placed in the /opt/ml/model directory inside the container.. Option C is correct because SageMaker expects the inference code to be in /opt/ml/model/ directory. Option A is wrong because ECR permissions would cause a different error. Option B is wrong because model artifacts are separate. Option D is wrong because an incorrect Region would cause a different error.

Answer

The container is not compatible with the SageMaker inference environment.

Answer

The SageMaker execution role does not have ECR pull permissions.

Answer

The model artifacts are not in the correct format.

Question 11

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?

Accepted Answer

Configure the tuning job with early stopping enabled.. Option B is correct because SageMaker's automatic model tuning supports early stopping with training job early stopping type. Option A is wrong because SageMaker Experiments is for tracking, not stopping. Option C is wrong because SageMaker Debugger stops training jobs, not tuning jobs. Option D is wrong because a CloudWatch alarm cannot stop a tuning job directly.

Answer

Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.

Answer

Use SageMaker Experiments to monitor and manually stop the tuning job.

Answer

Use SageMaker Debugger to stop training jobs that are not improving.

Question 12

A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU inference. The company wants to minimize latency and cost. Which instance type and deployment strategy should be used?

Accepted Answer

Use a real-time endpoint with a GPU instance and enable multi-model endpoints.. Option C is correct because SageMaker real-time endpoints with multi-model endpoints allow hosting multiple models on a single GPU instance, reducing cost while maintaining low latency. Option A is wrong because batch transform is not real-time. Option B is wrong because Serverless Inference does not support GPUs. Option D is wrong because asynchronous inference is not real-time.

Answer

Use a serverless inference endpoint with a GPU instance.

Answer

Use a batch transform job with a GPU instance.

Answer

Use an asynchronous inference endpoint with a GPU instance.

Question 13

A data scientist is training a model using Amazon SageMaker with a custom Docker container. The training job fails with an error: 'Resource exhausted: Out of memory'. The training data is stored in S3. What should the data scientist do to resolve this issue?

Accepted Answer

Increase the instance memory by selecting a larger instance type.. Option B is correct because 'Out of memory' indicates the instance does not have enough memory. Increasing the instance memory resolves the issue. Option A is wrong because using Pipe mode streams data directly from S3 and can reduce memory usage, but the error is about memory exhaustion, not data loading. Option C is wrong because EBS volume size does not affect memory. Option D is wrong because reducing batch size might help but is not the primary fix; increasing instance memory directly addresses the issue.

Answer

Increase the EBS volume size attached to the training instance.

Answer

Use Pipe mode for data loading instead of File mode.

Answer

Reduce the batch size in the training script.

Question 14

A machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?

Accepted Answer

Amazon SageMaker real-time endpoint. Option D is correct because Amazon SageMaker real-time endpoints are designed for low-latency, scalable, and highly available model hosting. Option A is wrong because AWS Lambda has limited execution time and is not suitable for heavy inference. Option B is wrong because Amazon ECS can host containers but requires more management; SageMaker is purpose-built. Option C is wrong because SageMaker batch transform is for offline predictions.

Answer

AWS Lambda

Answer

Amazon ECS with a custom container

Answer

Amazon SageMaker batch transform

Question 15

A company is using Amazon SageMaker to train a model. The training job is taking too long. The data scientist notices that the GPU utilization is low. Which action should be taken to improve training performance?

Accepted Answer

Increase the batch size in the training script.. Option C is correct because increasing the batch size can improve GPU utilization by keeping the GPU busy. Option A is wrong because increasing the number of instances may not help if each instance is underutilized. Option B is wrong because using spot instances can reduce cost but does not improve utilization. Option D is wrong because decreasing batch size would reduce utilization further.

Answer

Increase the number of training instances.

Answer

Use spot instances to reduce cost.

Answer

Decrease the batch size to reduce memory usage.