MLA-C01 Practice Test 21 — 15 Questions

Question 1

An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?

Accepted Answer

Pipeline caching. SageMaker Pipelines caching stores step outputs; if the step configuration and inputs are identical, the pipeline reuses the cached output, skipping execution.

Answer

Pipeline variable expressions

Answer

Model registry approval

Answer

Step parallelism

Question 2

A company uses SageMaker Model Registry to manage model versions. They want to automate the approval of models that pass automated evaluation, but require manual approval for others. Which Model Registry feature supports this workflow?

Accepted Answer

Approval workflow via pipeline Condition step. Model Registry supports approval statuses (PendingManualApproval, Approved, Rejected). Automated evaluation can set status to Approved, while borderline cases can be set to PendingManualApproval.

Answer

Cross-account deployment

Answer

Model versioning

Answer

Model lineage

Question 3

A machine learning engineer needs to deploy a model that requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?

Accepted Answer

Serverless inference with max concurrency set to 10. For low latency and occasional traffic, serverless inference is cost-effective because it scales to zero when not in use and charges per inference. Real-time endpoints incur cost even when idle, batch transform is for offline processing, and asynchronous inference has higher latency.

Answer

Asynchronous inference endpoint

Answer

Real-time endpoint on ml.g4dn.xlarge

Answer

Batch transform job

Question 4

A company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?

Accepted Answer

Asynchronous Inference. Asynchronous Inference is designed for large payloads, processes requests asynchronously, and supports SNS notifications on completion.

Answer

Batch Transform

Answer

Real-time endpoint

Answer

Serverless Inference

Question 5

A team is optimizing a deep learning model for deployment on SageMaker using SageMaker Neo. Which THREE of the following are valid optimization techniques that Neo can apply? (Choose THREE.)

Accepted Answer

Pruning (removing redundant weights). SageMaker Neo performs hardware-specific optimizations including quantisation (reducing precision), pruning (removing redundant weights), and operator fusion (combining operations). Knowledge distillation is a training-time technique, not part of Neo. Hyperparameter tuning is done by SageMaker Tuning jobs, not Neo.

Answer

Knowledge distillation

Answer

Hyperparameter tuning

Question 6

A team has a SageMaker Pipeline that trains a model and registers it in the Model Registry. They want to automate the deployment of the approved model to a staging environment. Which event-driven approach should they use?

Accepted Answer

Use Amazon EventBridge to listen for Model Registry approval events and trigger an AWS Lambda function that deploys the model. The Amazon EventBridge integration with SageMaker can trigger on Model Registry status changes (e.g., when a model version is approved). A Lambda function can then deploy the model to a staging endpoint. Step Functions can be used, but the trigger should be EventBridge. CloudWatch alarms are for monitoring metrics.

Answer

Use an SQS queue to store approval messages and have a cron job process them

Answer

Set up a CloudWatch alarm on the Model Registry's ApprovalStatus metric

Answer

Configure an AWS Step Functions state machine to poll the Model Registry every minute

Question 7

A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?

Accepted Answer

Multi-container endpoint. Multi-container endpoints allow running multiple containers, enabling preprocessing and inference in the same endpoint.

Answer

Asynchronous Inference

Answer

Multi-model endpoint

Answer

Real-time endpoint

Question 8

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

Accepted Answer

SMOTE on training set only. SMOTE should be applied only to the training set to avoid data leakage; evaluation must reflect the original distribution. Random undersampling may discard useful majority samples; random oversampling before split leaks information.

Answer

SMOTE on entire dataset before train/test split

Answer

Random oversampling of minority class before train/test split

Answer

Random undersampling of majority class

Question 9

An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?

Accepted Answer

SageMaker Neo. SageMaker Neo compiles trained models for specific hardware targets, including ARM CPUs, to optimize inference performance.

Answer

SageMaker Debugger

Answer

AWS Inferentia

Answer

Amazon Elastic Inference

Question 10

A machine learning engineer deploys a new model version to a SageMaker endpoint with production variants. They want to gradually shift traffic from the old model to the new model, monitoring for errors, and automatically roll back if the error rate exceeds 5%. Which deployment pattern should they use?

Accepted Answer

Canary deployment with CloudWatch alarms. Canary deployments gradually shift traffic and allow automated rollback based on CloudWatch alarms. Blue/green switches all at once. A/B testing is for comparing variants. Shadow testing mirrors traffic but doesn't serve the new model to users.

Answer

A/B testing with traffic splitting

Answer

Blue/green deployment

Answer

Shadow testing

Question 11

A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. Which SageMaker inference option should they use?

Accepted Answer

SageMaker Batch Transform. SageMaker Batch Transform is designed for batch inference on large datasets stored in S3, processing data in chunks and writing results to S3. It is cost-effective for non-real-time scenarios. Real-time endpoints are for low-latency inference. Serverless is for on-demand, not batch. Asynchronous is for near-real-time with S3 input/output but still not ideal for large batch jobs.

Answer

SageMaker real-time endpoint

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Question 12

A team uses SageMaker real-time endpoints for inference. They want to deploy a new model version and compare its performance with the current version under live traffic without affecting user experience. Which method should they use?

Accepted Answer

Shadow testing with SageMaker. Shadow testing (or shadow deployment) sends a copy of live traffic to the new model variant while the current variant serves the actual response. The shadow variant's performance can be monitored without impacting the user.

Answer

A/B testing with production variant traffic splitting

Answer

Batch transform on a holdout test set

Answer

Blue/green deployment

Question 13

A company wants to deploy a single model that processes images from a production line. The images are uploaded to an S3 bucket every few minutes, and the inference results must be stored back to S3. The team wants to avoid paying for idle compute and prefers a fully managed, on-demand solution. Which SageMaker inference option should they use?

Accepted Answer

SageMaker asynchronous inference. Asynchronous inference is designed for this use case: it processes images from S3 input, writes results to S3 output, scales to zero when idle, and is fully managed. Real-time endpoints are always running and incur cost when idle. Batch transform is not event-driven. Serverless inference is event-driven but has a payload limit and cold start that may not be suitable for image payloads.

Answer

SageMaker batch transform

Answer

SageMaker real-time endpoint with auto scaling

Answer

SageMaker serverless inference

Question 14

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

Accepted Answer

AWS Step Functions. A solution: Amazon EventBridge detects S3 events (new data), invokes a Lambda function that checks model performance (e.g., via SageMaker Model Monitor or custom metrics), and then starts a Step Functions workflow if degradation is detected. The other services: SageMaker Pipelines could replace Step Functions but is not listed as an option; SageMaker Model Monitor can track performance but is not an event source; CloudWatch Logs is not directly involved in the trigger logic.

Answer

Amazon CloudWatch Logs

Answer

SageMaker Model Registry

Question 15

A machine learning team needs to ensure that all model training and inference jobs within SageMaker Studio run in a private network without internet access. The team also requires that inter-container traffic within the same training job be encrypted. Which configurations should they combine?

Accepted Answer

Enable network isolation mode and inter-container traffic encryption. VPC-only mode restricts all traffic to the VPC, and inter-container traffic encryption ensures data in transit within the job is encrypted. These two settings together meet the requirements.

Answer

Configure SageMaker Studio in VPC-only mode and use KMS encryption

Answer

Use a VPC with a NAT gateway and enable network isolation

Answer

Enable inter-container traffic encryption and use a VPC with VPC endpoints