MLA-C01 Timed Practice Test 5 — 10 Questions

Question 1

A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?

Accepted Answer

Partition the data by a frequently filtered column, such as date. Partitioning by a frequently filtered column, such as date, allows Athena to use partition pruning. When a query includes a filter on the partition column, Athena can skip entire directories of data in S3, drastically reducing the amount of data scanned and improving query performance while also lowering cost.

Answer

Use uncompressed CSV files for simplicity

Answer

Partition the data by every column to maximize filtering

Question 2

A company is using an AWS Step Functions state machine to orchestrate a multi-step ML deployment. The workflow includes: training a model, evaluating it, registering the model, and deploying to a staging endpoint. They need to implement an approval gate before deploying to production. Which THREE components are necessary to achieve this? (Choose three.)

Accepted Answer

A task in the state machine that pauses and waits for manual approval via SNS or Lambda. Option B is correct because Step Functions can use a task with a callback pattern (`.waitForTaskToken`) to pause the workflow and wait for external manual approval. When combined with an SNS topic or Lambda function that sends a task success or failure signal back to Step Functions, this creates a reliable approval gate. This pattern allows the state machine to halt execution until a human approves or rejects the deployment, which is essential for production deployment control.

Answer

An AWS CodePipeline pipeline with approval stage

Answer

An Amazon SNS topic for notification of approval status

Question 3

A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?

Accepted Answer

SageMaker Serverless Inference. SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.

Answer

SageMaker Real-Time Endpoint with Auto Scaling

Answer

SageMaker Multi-Model Endpoint

Answer

SageMaker Batch Transform

Question 4

A company is deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?

Accepted Answer

Use SageMaker Multi-Model Endpoint. SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.

Answer

Use multiple single-model endpoints behind an Application Load Balancer

Answer

Use SageMaker Batch Transform for some models

Answer

Use a SageMaker Endpoint with multiple production variants

Question 5

A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?

Accepted Answer

Use SageMaker Batch Transform with separate jobs for each model.. SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.

Answer

Use SageMaker Pipelines to run inference as part of the pipeline.

Answer

Create a single SageMaker endpoint for all models and update the model periodically.

Answer

Deploy each model to a separate SageMaker endpoint and delete after use.

Question 6

Refer to the exhibit. A company configures a SageMaker Model Monitor Data Quality monitoring schedule as shown. The schedule runs every hour. However, the team notices that the monitoring job fails intermittently with an AccessDenied error when accessing the S3 bucket for output. The IAM role SageMakerMonitorRole has permissions to write to s3://my-bucket/monitor-output. What is the MOST likely cause of the failure?

Accepted Answer

The monitoring job runs in a VPC that does not have an S3 VPC endpoint, and the bucket policy denies requests from outside the VPC.. The intermittent AccessDenied error when SageMaker Model Monitor attempts to write to the S3 output bucket strongly indicates a network or policy restriction. If the monitoring job is configured to run inside a VPC (common for security compliance) and that VPC lacks an S3 VPC endpoint, traffic to S3 traverses the public internet. If the S3 bucket policy explicitly denies requests from outside the VPC (using a condition like `aws:SourceVpce` or `aws:SourceVpc`), then jobs running inside the VPC without an endpoint will be denied access intermittently, especially if the job's execution role is assumed from within the VPC.

Answer

The S3UploadMode is set to Continuous, which is only supported for batch transform jobs.

Answer

The cron expression is invalid; it should use rate(1 hour) instead.

Answer

The baseline constraints and statistics files are missing from the S3 bucket.

Question 7

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Accepted Answer

sagemaker:InvokeEndpoint. To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Answer

sagemaker:DescribeEndpoint

Answer

sagemaker:CreateEndpoint

Answer

sagemaker:ListEndpoints

Question 8

Refer to the exhibit. A SageMaker Pipeline fails with 'Invalid output reference' at the TrainingStep. What is the most likely cause?

Accepted Answer

The TrainingStep expects a single artifact but TuningStep produces multiple. Option C is correct because in SageMaker Pipelines, a `TrainingStep` that expects a single artifact as input will fail with 'Invalid output reference' if the preceding `TuningStep` produces multiple artifacts (e.g., from multiple training jobs). The pipeline cannot resolve which specific artifact to pass, causing the error.

Answer

The TuningStep output name is misspelled

Answer

The pipeline role lacks permissions

Answer

The instance type is incompatible

Question 9

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has a limit of 0 for ml.p3.2xlarge instances.. The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.

Answer

The volume size is too small for training.

Answer

The Preprocess step did not complete successfully.

Answer

The training image is not accessible.

Question 10

A company deploys a model on Amazon SageMaker for real-time inference. The inference latency is too high. The model is a large deep learning model. The company wants to reduce latency without significantly impacting accuracy. Which approach should the company consider?

Accepted Answer

Use SageMaker Inference Recommender to test different instance types and optimizations.. SageMaker Inference Recommender is designed specifically to automate load testing and benchmarking across various instance types and model optimizations (e.g., Elastic Inference, GPU acceleration, serialization formats). It provides latency and throughput metrics to identify the optimal configuration for reducing inference latency while maintaining accuracy, making it the correct choice for a large deep learning model with high latency.

Answer

Increase the batch size for inference.

Answer

Use a smaller instance type to reduce inference time.

Answer

Enable SageMaker Model Monitor to detect performance issues.