MLA-C01 Practice Test 19 — 15 Questions

Question 1

A team is using Amazon SageMaker's Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. After 10 training jobs, the best objective metric value plateaus. The team wants to explore the search space more broadly. Which AMT strategy should they use?

Accepted Answer

Random search. Random search samples hyperparameters randomly and covers the search space more broadly, which can help escape a plateau. Bayesian optimization focuses on promising regions, which may not explore broadly. Grid search is exhaustive but expensive. Hyperband uses early stopping to allocate resources efficiently but still may not explore broadly if the plateau persists.

Answer

Grid search

Answer

Bayesian optimization

Answer

Hyperband

Question 2

A company deploys a real-time inference endpoint on SageMaker for a customer-facing application. Traffic patterns are unpredictable and sometimes spike. The endpoint must scale automatically to handle load while minimizing cost. Which approach should the company take?

Accepted Answer

Configure a target tracking scaling policy on the endpoint using CloudWatch metrics.. Option D is correct because a target tracking scaling policy with a metric like average latency or request count scales automatically based on demand. Option A is wrong because using multiple models does not address scaling. Option B is wrong because increasing instance type without auto-scaling leads to over-provisioning. Option C is wrong because batch transform is for asynchronous, not real-time.

Answer

Switch to batch transform for all inference requests.

Answer

Use a larger instance type to handle peak traffic.

Answer

Deploy multiple models behind an Application Load Balancer.

Question 3

Which of the following is a recommended practice for preparing training data in Amazon SageMaker?

Accepted Answer

Convert data to RecordIO format for built-in algorithms. RecordIO format is recommended for built-in algorithms as it improves I/O performance. Storing data in Amazon DynamoDB is not suitable for training datasets. Public read access on S3 is insecure. While gzip compression is common, RecordIO is a specific best practice for SageMaker.

Answer

Store training data in Amazon DynamoDB

Answer

Compress data using gzip to reduce transfer time

Answer

Use Amazon S3 with public read access

Question 4

A data engineer needs to split a time-series dataset into training and validation sets for a forecasting model. Which split method should be used to avoid data leakage?

Accepted Answer

Temporal split where training uses data up to a cutoff date and validation uses later data.. Option B is correct because for time-series data, a temporal split ensures that validation data comes from a later time period than training data. Option A is wrong because random splits can cause future data to leak into training. Option C is wrong while cross-validation is useful, it must be done in a time-aware manner (e.g., rolling origin), but standard k-fold cross-validation is not appropriate. Option D is wrong because feature importance is not a splitting method.

Answer

Use k-fold cross-validation with random shuffling.

Answer

Use feature importance scores to weight the splitting process.

Answer

Random split with 80% training and 20% validation.

Question 5

Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)

Accepted Answer

Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).. Options A, C, and D are correct. Using EFA (Elastic Fabric Adapter) reduces network latency, choosing the right distribution strategy (e.g., data parallelism vs model parallelism) improves scaling, and using GPU-optimized instances provides high compute. Option B is wrong because attaching additional EBS volumes does not directly help distributed training performance. Option E is wrong because batch transform is for inference, not training.

Answer

Attach multiple EBS volumes with throughput provisioning.

Answer

Enable batch transform for offline inference after training.

Question 6

A team is using SageMaker Pipelines to automate a training workflow. They need to ensure that if a step fails, the pipeline can resume from the failed step without reprocessing prior steps. Which TWO configurations are necessary? (Choose TWO.)

Accepted Answer

Set a retry policy on the step. Options B and D are correct. Enabling caching on each step (B) allows outputs to be reused from previous runs. Setting a retry policy (D) allows the pipeline to retry the failed step. Option A is wrong because parallelism does not affect resumption. Option C is wrong because Lambda steps are for custom processing, not resumption. Option E is wrong because storing artifacts is common but not sufficient for resumption without caching.

Answer

Set the Pipeline's parallel flag to True

Answer

Use a Lambda step for retry logic

Answer

Store intermediate artifacts in S3

Question 7

A company uses SageMaker to train a model with a large dataset stored in S3. They notice that the training job is taking longer than expected and the GPU utilization is low. Which action would most likely improve GPU utilization?

Accepted Answer

Increase the batch size. Option D is correct because increasing the batch size can help saturate GPU compute. Options A is wrong because decreasing batch size would lower GPU utilization. B is wrong because using a smaller instance provides less compute. C is wrong because disabling distributed training reduces parallelism.

Answer

Disable distributed training

Answer

Use a smaller instance type

Answer

Decrease the batch size

Question 8

A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?

Accepted Answer

Switch to a larger instance type. Option B is correct because switching to a larger instance type provides more compute capacity, reducing inference latency. Option A is wrong because multi-model endpoints may increase latency due to model loading. C is wrong because Batch Transform is for batch, not real-time. D is wrong because Elastic Inference adds GPU acceleration but may not reduce latency as much as compute upgrade.

Answer

Use a multi-model endpoint

Answer

Add Elastic Inference

Answer

Enable SageMaker Batch Transform

Question 9

A data scientist is training a deep learning model on SageMaker and notices that the training loss oscillates and does not converge. They want to debug this issue. Which SageMaker feature can they use to monitor and analyze the training process?

Accepted Answer

SageMaker Debugger. Option A is correct because SageMaker Debugger provides monitoring, visualization, and built-in rules to detect issues like oscillating loss. Option B is wrong because Automatic Model Tuning is for hyperparameter search, not real-time monitoring. Option C is wrong because SageMaker Profiler focuses on resource utilization. Option D is wrong because Gradient Descent is a method, not a feature.

Answer

SageMaker Profiler

Answer

SageMaker Gradient Descent optimization

Answer

SageMaker Automatic Model Tuning

Question 10

A data science team deploys a real-time inference endpoint on Amazon SageMaker. They want to monitor for data drift in the input features over time. Which AWS service should they use to capture and analyze the input data distribution?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is designed to detect data drift by capturing and analyzing input data distributions. CloudWatch Logs is for logging, CloudTrail for API auditing, and Athena for ad-hoc querying.

Answer

Amazon Athena

Answer

AWS CloudTrail

Answer

Amazon CloudWatch Logs

Question 11

A company uses SageMaker endpoints with auto-scaling. The endpoint is experiencing high latency during peak hours. The metrics show CPU utilization is low but memory is high. What is the most likely cause?

Accepted Answer

The auto-scaling policy is based on CPU utilization, which does not trigger scaling.. The auto-scaling policy is likely based on CPU utilization, which does not trigger scaling during memory pressure. Memory leaks could be a secondary cause, but the primary issue is the scaling metric.

Answer

The model is not optimized for inference, causing memory leaks.

Answer

The instance type has insufficient network bandwidth.

Answer

The endpoint is deployed in a VPC without a NAT gateway.

Question 12

An ML team trained a model using SageMaker and stored the model artifacts in S3 with server-side encryption using AWS KMS (SSE-KMS). They need to deploy the model to a SageMaker endpoint that uses a different KMS key for inference data encryption. What must they do to ensure the endpoint can decrypt the model artifacts?

Accepted Answer

Grant the SageMaker execution role access to both KMS keys.. The SageMaker execution role must have kms:Decrypt permission on the KMS key used for model artifacts, and also kms:Encrypt and kms:Decrypt on the key for inference data. Providing the same key is not required.

Answer

Provide the same KMS key for both model artifacts and inference data.

Answer

Use a customer-managed key (CMK) with the same key material.

Answer

Configure the endpoint to use SSE-S3 instead of SSE-KMS.

Question 13

A company wants to audit all API calls made to SageMaker endpoints for security compliance. Which AWS service should they enable?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records all API calls for auditing. GuardDuty is for threat detection, Macie for sensitive data discovery, and Config for configuration changes.

Answer

Amazon GuardDuty

Answer

AWS Config

Answer

AWS CloudTrail

Question 14

A machine learning model is deployed on SageMaker and its predictions are used in a production application. The model's accuracy has degraded over time. What is the most likely cause?

Accepted Answer

The model experienced concept drift.. Concept drift occurs when the statistical properties of the target variable change over time, leading to accuracy degradation. Training data shuffling and instance size affect training performance and latency, not accuracy post-deployment.

Answer

The training data was not shuffled properly.

Answer

The model was not compiled for inference.

Answer

The endpoint instance type is too small.

Question 15

A company uses SageMaker training jobs that need to access data in an S3 bucket in a different AWS account. The bucket uses a bucket policy that allows access only from a specific VPC. How should they configure the training job?

Accepted Answer

Use cross-account access with an IAM role and add a bucket policy allowing the training job's VPC.. The training job should be launched in a VPC with an S3 VPC endpoint, and the bucket policy must allow the VPC. Additionally, an IAM role in the source account with cross-account trust is needed. Option C combines both requirements.

Answer

Use AWS DataSync to copy data to the training account's S3.

Answer

Create an IAM role in the source account and assume it from the training account.

Answer

Use an S3 VPC endpoint in the training job's VPC and attach a bucket policy that allows the VPC.