MLS-C01 Machine Learning Implementation and Operations Practice Test 9 — 15 Questions

Question 1

A team uses AWS Glue ETL jobs to preprocess data for SageMaker training. The job runs successfully but the output data is empty. What is the most likely cause?

Accepted Answer

The filter transformation condition is too restrictive, removing all rows. A filter transformation in AWS Glue ETL jobs can remove all rows if the condition is too restrictive, resulting in an empty output dataset. This is a common logic error where the filter predicate (e.g., `df.filter("value > 100")`) matches no records, causing the DynamicFrame to be empty after transformation. The job succeeds because no runtime error occurs, but the output is empty.

Answer

There is a data type mismatch between source and target

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The source data is partitioned and only a subset of partitions is read

Answer

The Glue job runs out of memory and fails silently

Question 2

A company uses SageMaker to host a model for real-time predictions. The model is updated weekly. To minimize downtime during model updates, what should the company do?

Accepted Answer

Create a new endpoint configuration with the new model and update the endpoint to use the new configuration. SageMaker allows you to create a new endpoint configuration with the updated model and then update the existing endpoint to use this new configuration. This triggers a rolling update where SageMaker gradually shifts traffic from the old model to the new one, ensuring zero downtime during the transition. The endpoint remains available throughout the process, and you can roll back quickly if needed by reverting to the previous configuration.

Answer

Create a second endpoint with the new model and use an Application Load Balancer to route traffic

Answer

Update the existing endpoint configuration with the new model URL

Answer

Delete the existing endpoint and create a new one with the updated model

Question 3

A machine learning team is using SageMaker Processing jobs to run feature engineering on large datasets. The job takes a long time to complete. Which change would most likely reduce the processing time?

Accepted Answer

Increase the number of instances in the processing cluster. Increasing the number of instances in the processing cluster enables SageMaker Processing to distribute the workload across multiple nodes, leveraging parallel processing to reduce the overall execution time. SageMaker Processing uses a distributed computing model where each instance processes a subset of the data, so adding more instances directly increases parallelism and throughput for embarrassingly parallel tasks like feature engineering.

Answer

Switch to local mode to avoid network overhead

Answer

Change the processing script from Python to PySpark

Answer

Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge

Question 4

A company is using SageMaker to train a model. The training data includes personally identifiable information (PII). The company must ensure that the data is encrypted at rest and in transit. Which combination of actions meets these requirements?

Accepted Answer

Use S3 server-side encryption and enable SageMaker inter-container traffic encryption. It combines S3 server-side encryption (SSE-S3) to protect data at rest in the training dataset with SageMaker inter-container traffic encryption, which encrypts data in transit between training containers using TLS. This dual approach satisfies the requirement for encryption both at rest and in transit within the SageMaker training environment.

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3)

Answer

Enable SSL for data in transit and use VPC endpoints

Answer

Place all resources in a private VPC subnets with no internet access

Question 5

A data scientist wants to use AWS Step Functions to orchestrate a machine learning workflow including data preprocessing, training, and evaluation. Which SageMaker integration is best suited for this purpose?

Accepted Answer

Use the SageMaker SDK with Step Functions service integrations. AWS Step Functions has a native service integration with SageMaker, allowing you to directly call SageMaker API actions (e.g., CreateTrainingJob, CreateModel, CreateEndpointConfig, CreateEndpoint) without needing intermediate Lambda functions. This integration supports both synchronous and asynchronous invocations, making it ideal for orchestrating a multi-step ML workflow with built-in error handling, retries, and state management.

Answer

Implement each step as an AWS Lambda function and call Step Functions

Answer

Use SageMaker Pipelines to define the workflow

Answer

Use AWS Batch to run the steps sequentially

Question 6

A company is using SageMaker to host a model that makes predictions on streaming data from Amazon Kinesis. The model must provide predictions with sub-second latency. Which approach should the company use?

Accepted Answer

Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis. A SageMaker real-time endpoint provides sub-second latency for individual predictions, and invoking it from an AWS Lambda function triggered by Kinesis allows each streaming record to be processed synchronously with low overhead. This architecture meets the requirement for low-latency predictions on streaming data.

Answer

Use SageMaker asynchronous inference with a Kinesis trigger

Answer

Use Amazon Kinesis Data Analytics with a built-in ML model

Answer

Use SageMaker batch transform to process batches of records from Kinesis

Question 7

A team is using SageMaker to train a model. They want to track hyperparameters, metrics, and model artifacts. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct choice because it is purpose-built for tracking hyperparameters, metrics, and model artifacts across training runs. It automatically captures input parameters, output metrics, and artifact locations (e.g., S3 paths) for each trial, enabling comparison and lineage tracking without manual logging.

Answer

SageMaker Pipelines

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SageMaker Debugger

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SageMaker Model Registry

Question 8

A company is deploying a SageMaker model for real-time inference. The endpoint must be highly available and cost-effective. Which TWO actions should the company take? (Select TWO.)

Accepted Answer

Deploy the endpoint with at least two instances in different Availability Zones. Deploying a SageMaker endpoint with at least two instances in different Availability Zones (AZs) ensures high availability by eliminating a single point of failure. If one AZ goes down, traffic is automatically routed to the healthy instance in the other AZ, meeting the requirement for a highly available real-time inference endpoint.

Answer

Use managed spot training for inference

Answer

Use GPU instances for all models even if not required

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Use a single large instance to handle peak load

Question 9

A data scientist is using SageMaker to train a model. The training job needs to access data in an S3 bucket in a different AWS account. The data scientist has set up proper S3 bucket policies and IAM roles. Which THREE steps are necessary to allow SageMaker to access the cross-account S3 bucket? (Select THREE.)

Accepted Answer

Configure the S3 bucket policy to grant access to the SageMaker execution role ARN from the training account. The S3 bucket policy in the data account must explicitly grant the SageMaker execution role ARN from the training account the necessary permissions (e.g., s3:GetObject, s3:ListBucket). This is the foundational step for cross-account access, as S3 bucket policies are resource-based policies that can specify principals from other AWS accounts.

Answer

Create a VPC endpoint for S3 in the training account

Answer

Use an AWS KMS key to encrypt the data in transit

Question 10

A company uses SageMaker to train a model. The training job is taking too long and the data scientist wants to speed it up. Which THREE strategies should the data scientist consider? (Select THREE.)

Accepted Answer

Use a GPU instance type like ml.p3.2xlarge. GPU instances like ml.p3.2xlarge are optimized for parallel computation, significantly accelerating the training of deep learning models by handling matrix operations more efficiently than CPUs. SageMaker supports a range of GPU instances (e.g., ml.p3, ml.p4, ml.g5) that can reduce training time for compute-intensive workloads.

Answer

Reduce the number of training epochs

Answer

Increase the batch size in the training script

Question 11

A data scientist attempts to create a SageMaker training job using the IAM policy shown in the exhibit. The training job fails with an access denied error. What is the most likely cause?

Accepted Answer

The IAM policy is missing the s3:ListBucket action. The IAM policy shown in the exhibit grants s3:GetObject and s3:PutObject actions but is missing the s3:ListBucket action. When SageMaker attempts to read or write objects in an S3 bucket, it first needs to list the bucket's contents to verify the object's existence and path. Without s3:ListBucket, the training job fails with an access denied error during the initial S3 operation.

Answer

The S3 bucket policy does not grant access to the SageMaker service principal

Answer

The IAM policy is missing the sagemaker:DescribeTrainingJob permission

Answer

The IAM policy is missing the s3:PutObject permission

Question 12

A SageMaker training job fails with the failure reason shown in the exhibit. What is the most likely cause?

Accepted Answer

There is an error in the custom training script. The failure reason in the exhibit (not shown here but implied by the question) typically indicates a runtime error such as a Python exception, missing module, or syntax error in the custom training script. SageMaker logs the exact error from the container, and when the script itself fails, the training job terminates with a 'ClientError' or 'AlgorithmError' referencing the script, not infrastructure issues.

Answer

The training instance ran out of memory

Answer

The S3 bucket with training data is not accessible

Answer

The SageMaker service limit for the instance type has been exceeded

Question 13

A SageMaker endpoint has a CloudWatch alarm configured as shown in the exhibit. The alarm fires when the p99 latency exceeds 500 ms for two consecutive minutes. Which action should the data scientist take to reduce latency?

Accepted Answer

Increase the number of instances behind the endpoint. Increasing the number of instances behind the endpoint adds more compute capacity to handle the inference requests, which directly reduces the queuing and processing time for each request. Since the alarm triggers when p99 latency exceeds 500 ms for two consecutive minutes, scaling out horizontally distributes the load and lowers tail latency.

Answer

Increase the batch size in the inference request

Answer

Use SageMaker asynchronous inference instead of real-time

Answer

Switch to GPU instances even if the model does not require GPU

Question 14

A data scientist is training a model on Amazon SageMaker and notices that the training job is taking much longer than expected. The instance type is ml.m5.xlarge and the dataset is 10 GB in CSV format. Which action is MOST likely to reduce training time without changing the instance type?

Accepted Answer

Convert the dataset to RecordIO or Parquet format before training.. Converting the dataset from CSV to a columnar format like Parquet or RecordIO reduces I/O overhead and improves data throughput during training. SageMaker's built-in algorithms and many deep learning frameworks can read these formats more efficiently, especially for large datasets, because they enable better compression and predicate pushdown, reducing the time spent on data loading.

Answer

Change the instance type to ml.p3.2xlarge (GPU) for faster computation.

Answer

Reduce the number of training epochs to speed up convergence.

Answer

Increase the batch size to the maximum supported by the instance memory.

Question 15

A machine learning engineer is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency (under 100 ms) and high throughput (1000 requests per second). Which SageMaker deployment option is MOST suitable?

Accepted Answer

Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.. SageMaker Inference Recommender runs load tests against the model to identify the optimal instance type, instance count, and endpoint configuration (e.g., container parameters, model server settings) that meet the specific latency and throughput requirements. For a large deep learning model demanding under 100 ms latency and 1000 requests per second, this automated benchmarking is essential to avoid over-provisioning or under-provisioning resources.

Answer

Deploy the model on a single endpoint with automatic scaling based on CPU utilization.

Answer

Use SageMaker Serverless Inference with provisioned concurrency.

Answer

Use a multi-model endpoint to load multiple copies of the model on the same instance.