MLS-C01 Machine Learning Implementation and Operations Practice Test 19 — 15 Questions

Question 1

A data scientist needs to deploy a model with a custom inference container. Which THREE requirements must the container meet for SageMaker hosting?

Accepted Answer

Implement a /ping endpoint for health checks. SageMaker requires containers to implement the /invocations and /ping endpoints, and to serve on port 8080. Options A, B, and E are correct. Option C is for training, Option D is optional.

Answer

Provide a training script at /opt/ml/input/data

Answer

Use the SageMaker Python SDK to load the model

Question 2

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 3

A data scientist is training a deep learning model on Amazon SageMaker using the built-in Object Detection algorithm. The training job is failing with a 'ResourceLimitExceeded' error when trying to launch multiple GPU instances. Which of the following is the MOST likely cause?

Accepted Answer

The account has reached the limit for the number of GPU instances in the current AWS Region.. Option D is correct because the 'ResourceLimitExceeded' error typically indicates that the requested instance type or count exceeds the account's service limit for SageMaker training instances. Option A is wrong because SageMaker does not enforce a limit on the total dataset size. Option B is wrong because S3 bucket permissions would cause a different error (e.g., AccessDenied). Option C is wrong because a corrupted training script would cause a different error (e.g., ModuleNotFoundError).

Answer

The training script has a syntax error.

Answer

The dataset is too large for the selected instance type.

Answer

The S3 bucket containing the training data has insufficient permissions.

Question 4

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 5

A company is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The data scientist wants to use the Pipe mode for training to stream data directly from S3 instead of downloading it first. Which of the following is a prerequisite for using Pipe mode?

Accepted Answer

The training data must be in RecordIO-protobuf or TFRecord format.. Option B is correct because Pipe mode requires the training data to be in a format that supports streaming, such as RecordIO or TFRecord. Option A is wrong because SageMaker can handle S3 data regardless of bucket policy. Option C is wrong because Pipe mode does not require data to be in a single file. Option D is wrong because Pipe mode does not require the data to be uncompressed.

Answer

The training data must be compressed using Gzip.

Answer

The S3 bucket must have public read access.

Answer

The training data must be stored as a single large file.

Question 6

A data scientist is performing hyperparameter tuning using Amazon SageMaker Automatic Model Tuning (AMT). The job uses a random search strategy. After 20 training jobs, the best objective metric value has plateaued. The data scientist wants to explore more of the hyperparameter space. Which action should the data scientist take?

Accepted Answer

Change the tuning strategy from Random to Bayesian.. Option C is correct because switching to Bayesian search will explore new regions based on previous results, potentially finding better hyperparameters. Option A is wrong because increasing the number of parallel jobs does not change the search strategy. Option B is wrong because decreasing the number of training jobs reduces exploration. Option D is wrong because early stopping does not change the search strategy.

Answer

Enable early stopping.

Answer

Decrease the maximum number of training jobs.

Answer

Increase the number of parallel training jobs.

Question 7

A company is using Amazon Forecast for demand forecasting. The data includes time series data for multiple items. The company wants to ensure that the forecast is updated daily as new data arrives. Which approach should be used to automate this process?

Accepted Answer

Use AWS Lambda to invoke the Forecast CreateDatasetImportJob and CreatePredictorBacktestExportJob APIs on a daily schedule triggered by Amazon CloudWatch Events.. Option A is correct because Amazon Forecast Predictor can be updated with new data by using the CreateDatasetImportJob API, and then retraining the predictor. Option B is wrong because Forecast does not support real-time streaming. Option C is wrong because Forecast does not update automatically; manual retraining is required. Option D is wrong because there is no built-in scheduler; custom automation is needed.

Answer

Use Amazon Kinesis Data Streams to stream new data directly into Forecast.

Answer

Use Amazon SageMaker to retrain the model daily and replace the forecast endpoint.

Answer

Enable the 'AutoPredictor' feature in Forecast to automatically update the predictor when new data arrives.

Question 8

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 9

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 10

A company is using Amazon SageMaker to train a model on data stored in S3. The training job needs to access data from an S3 bucket in a different AWS account. The data owner has granted cross-account access via a bucket policy. However, the training job fails with an AccessDenied error. What is the MOST likely cause?

Accepted Answer

The SageMaker execution role does not have the necessary permissions to access the S3 bucket.. Option A is correct because SageMaker training jobs use an execution role; that role must be granted cross-account access via the bucket policy. Option B is wrong because S3 does not require VPC endpoints for cross-account access. Option C is wrong because the data does not need to be public. Option D is wrong because KMS encryption is not the issue unless the bucket policy denies.

Answer

The data is encrypted with SSE-KMS and the SageMaker role lacks KMS permissions.

Answer

The S3 bucket is not configured with public access.

Answer

The S3 bucket is in a different region and requires a VPC endpoint.

Question 11

A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training script reads data from an S3 bucket and writes the model artifact to an S3 bucket. The training job fails with a 'NoSuchKey' error. What is the MOST likely cause?

Accepted Answer

The training data path specified in the input data channel is incorrect.. Option B is correct because the error indicates that the specified file or key does not exist in the S3 bucket. Option A is wrong because a missing ECR image would cause a different error. Option C is wrong because insufficient permissions would cause AccessDenied. Option D is wrong because the training image is not the issue.

Answer

The training script is not compatible with the Docker image.

Answer

The Docker image is not available in Amazon ECR.

Answer

The SageMaker execution role does not have s3:GetObject permission.

Question 12

A company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)

Accepted Answer

Grant the SageMaker execution role s3:GetObject permission for the data bucket.. Options A and D are correct. Option A: The IAM role must have s3:GetObject permission for the bucket. Option D: The input data channel must specify the S3 URI with the correct prefix. Option B is wrong because File mode does not require RecordIO. Option C is wrong because Pipe mode is not used. Option E is wrong because File mode does not require data in a single file.

Answer

Configure the training job to use Pipe mode.

Answer

Concatenate all data files into a single file.

Answer

Convert the data to RecordIO-protobuf format.

Question 13

A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives a large number of requests with variable traffic patterns. The team wants to minimize cost while ensuring low latency. Which THREE actions should the team take? (Choose THREE.)

Accepted Answer

Use a multi-model endpoint to host multiple models on the same instance.. Options A, C, and E are correct. Option A: Using a production variant with a smaller instance type reduces cost. Option C: Enabling auto-scaling adjusts capacity based on traffic. Option E: Using a multi-model endpoint allows sharing instances among models. Option B is wrong because higher concurrency may increase cost. Option D is wrong because a single large instance may be over-provisioned.

Answer

Set the initial variant weight to 1 and increase the number of instances.

Answer

Use a single large instance to handle all traffic.

Question 14

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 15

A company is deploying a machine learning model using SageMaker. The model is a PyTorch model that requires GPU for inference. The company wants to minimize costs while ensuring low latency. Which instance type should be used for the SageMaker endpoint?

Accepted Answer

ml.p3.2xlarge. Option B is correct because ml.p3.2xlarge is a GPU instance optimized for inference with low latency, and it is cost-effective for production workloads. Option A is wrong because ml.c5.2xlarge is a CPU instance and does not have a GPU. Option C is wrong because ml.g4dn.xlarge is also a GPU instance but may not provide the same performance for PyTorch models as ml.p3. Option D is wrong because ml.m5.large is a general-purpose CPU instance without GPU support.

Answer

ml.m5.large

Answer

ml.c5.2xlarge

Answer

ml.g4dn.xlarge