MLA-C01 Practice Test 30 — 15 Questions

Question 1

A machine learning engineer is building a real-time fraud detection pipeline using Amazon Kinesis Data Streams. The data must be prepared (e.g., feature engineering, normalization) before being fed into a SageMaker endpoint. Which TWO steps should the engineer implement to ensure low-latency data preparation?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use AWS Lambda functions to apply feature transformations on each record as it arrives.. AWS Lambda can process records from Kinesis in near real-time for lightweight transformations like normalization and feature engineering. For streaming data, SageMaker batch transform is not real-time. Glue ETL is batch-oriented and adds latency. Amazon Kinesis Data Analytics can perform SQL-based transformations in real-time. SageMaker Processing jobs are designed for offline processing.

Answer

Use SageMaker batch transform jobs scheduled every hour to process the streaming data.

Answer

Use AWS Glue ETL jobs running on a recurring schedule to transform the data.

Answer

Use SageMaker Processing jobs to read from Kinesis and write transformed data to S3.

Question 2

A company uses Amazon SageMaker Feature Store to store features for a real-time recommendation model. The feature data is updated continuously, and the model must use the most recent feature values for each user at inference time. Which type of Feature Store should the company use for serving features to the model?

Accepted Answer

Online store. Online store provides low-latency access to the latest feature values for real-time inference. Offline store is for batch training and analytics. Point-in-time queries are for historical retrieval, not real-time serving.

Answer

Offline store

Answer

Point-in-time queries

Answer

Both online and offline store

Question 3

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection model. The dataset contains 1 million images, and the team wants to reduce labeling cost by labeling only the most informative samples. Which feature of Ground Truth should they use?

Accepted Answer

Active learning. Ground Truth offers active learning, which automatically selects the most informative samples to label, reducing cost. Option A is correct. Options B, C, and D do not provide automatic sample selection for labeling.

Answer

Automated data labeling

Answer

Pre-built annotation worker UI

Answer

Consolidated labeling

Question 4

A data scientist is preparing a dataset for binary classification. The dataset has a target variable with 90% of samples belonging to class 0 and 10% to class 1. Which data splitting strategy should the scientist use to ensure that the training and test sets maintain the same class proportion as the original dataset?

Accepted Answer

Stratified sampling. Stratified sampling ensures that each class is proportionally represented in the splits. Random splitting may not preserve the ratio; k-fold CV and time-series split are not appropriate for this requirement.

Answer

Time-series split

Answer

Simple random split

Answer

k-fold cross-validation

Question 5

A company is training a deep learning model on Amazon SageMaker using a dataset stored in Amazon S3. The training job is taking a long time due to I/O bottlenecks. The data is in JSON lines format. Which data preparation step combined with SageMaker's best practices would most effectively reduce training time?

Accepted Answer

Convert the data to RecordIO-Protobuf format and use SageMaker's Pipe mode for training.. Converting JSON lines data to RecordIO-Protobuf format allows SageMaker's Pipe mode to stream data directly from Amazon S3 to the training algorithm without writing to disk, eliminating I/O bottlenecks. Pipe mode uses a FIFO pipe (named pipe) to feed data sequentially, which significantly reduces training time for deep learning models that iterate over the dataset multiple times.

Answer

Convert the JSON lines files to CSV format and use SageMaker's File mode for training.

Answer

Compress the JSON lines files using gzip and use File mode with local caching.

Answer

Split the data into multiple smaller files and use multiple training instances to parallelize.

Question 6

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 7

A SageMaker Processing job fails with 'Access Denied' when listing objects in an S3 bucket, despite the IAM policy shown in the exhibit. What is the most likely cause?

Accepted Answer

The policy lacks `s3:ListBucket` permission.. The error 'Access Denied' when listing objects in an S3 bucket indicates that the IAM role used by the SageMaker Processing job lacks the `s3:ListBucket` permission. This permission is required for the `ListObjectsV2` API call, which is necessary to enumerate objects in the bucket. Even if the role has `s3:GetObject` and `s3:PutObject` permissions, without `s3:ListBucket`, the job cannot list the bucket contents and will fail with an access denied error.

Answer

The role does not have a trust relationship with SageMaker.

Answer

The bucket policy denies the access.

Answer

The bucket is in a different region.

Question 8

A company uses an Amazon SageMaker endpoint for real-time inference. The security team requires that all traffic between the endpoint and the client application be encrypted in transit. Which configuration ensures this?

Accepted Answer

The endpoint is automatically served over HTTPS; no additional configuration is needed.. Amazon SageMaker endpoints are automatically served over HTTPS, which encrypts all data in transit between the client application and the endpoint. This is a default behavior of SageMaker real-time inference endpoints, so no additional configuration is required to meet the encryption-in-transit requirement.

Answer

Deploy the endpoint in a VPC and use VPC Endpoints.

Answer

Use AWS Key Management Service (KMS) to encrypt the data in transit.

Answer

Attach an AWS Certificate Manager (ACM) certificate to the endpoint.

Question 9

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 endpoint needs to decrypt the model artifacts stored with SSE-KMS using the original KMS key, and then re-encrypt the inference data with a different KMS key. The SageMaker execution role must have kms:Decrypt permission on the key used for the model artifacts and kms:Encrypt permission on the key used for inference data encryption. Without granting access to both keys, the endpoint cannot read the model artifacts or encrypt the output.

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 10

A machine learning team at a retail company has deployed a product recommendation model using Amazon SageMaker. The model is updated weekly with new data. Recently, the team noticed that the model's accuracy on a holdout evaluation set has been declining over the past month. The data pipeline that feeds the training job has not changed. The team suspects data drift. They have SageMaker Model Monitor enabled on the inference endpoint and have set up Amazon CloudWatch metrics for feature distribution distances. Upon reviewing the CloudWatch dashboards, they see that the feature distribution distance metric for the most important feature 'product_category' has increased significantly. However, the team is unsure if this is the root cause. Which remediation step should the team take FIRST?

Accepted Answer

Investigate the data pipeline that feeds the training job to ensure consistent data collection and encoding of the 'product_category' feature. The first step when data drift is suspected is to investigate the data pipeline to ensure consistent data collection and encoding. Since the model's accuracy is declining and the feature distribution distance for 'product_category' has increased, the root cause may be a change in how the feature is collected or encoded upstream, not necessarily a change in the underlying data distribution. SageMaker Model Monitor detects drift in feature distributions, but it cannot diagnose the cause; the team must verify the pipeline before retraining or modifying the model.

Answer

Retrain the model using the most recent week of data and redeploy to the endpoint

Answer

Rebuild the SageMaker endpoint with a different instance type to improve performance

Answer

Reduce the number of features in the model by removing 'product_category'

Question 11

A team deploys a PyTorch model on Amazon SageMaker for real-time inference. They notice that inference latency is higher than expected. They suspect the serialization format used for input data is inefficient. Which approach would MOST likely reduce latency?

Accepted Answer

Change the input serialization format to Protocol Buffers.. Protocol Buffers (protobuf) are a binary serialization format that is significantly more compact and faster to parse than text-based formats like JSON or CSV. By reducing the size of the input data and the CPU overhead of deserialization, switching to protobuf directly addresses the root cause of high inference latency on SageMaker real-time endpoints.

Answer

Use Amazon SageMaker Batch Transform instead of real-time inference.

Answer

Enable automatic scaling on the endpoint.

Answer

Increase the instance type to a compute-optimized instance.

Question 12

A company uses SageMaker Model Monitor to detect drift. They want to receive notifications when drift is detected. Which TWO services can be used together to send notifications? (Choose TWO.)

Accepted Answer

Amazon SNS topics to send email or SMS.. Amazon SNS (Simple Notification Service) is the correct service to send notifications via email or SMS when drift is detected. Amazon CloudWatch Alarms can be set on the drift metrics emitted by SageMaker Model Monitor (e.g., `feature_baseline_drift` or `data_quality_drift`), and when the alarm state changes to ALARM, it can trigger an SNS topic to deliver the notification. This combination provides a fully managed, serverless notification pipeline without custom code.

Answer

Amazon EventBridge to trigger a notification.

Answer

AWS Lambda to process the drift and send an email via SES.

Answer

Amazon SQS to queue the notification.

Question 13

An ML engineer is preparing a time-series dataset for a forecasting model that predicts daily sales for the next 30 days. The dataset contains 3 years of daily sales data. Which data splitting strategy should the engineer use to evaluate the model's performance on future data?

Accepted Answer

Walk-forward validation (time-series split). Walk-forward validation (also called time-series cross-validation) respects temporal order and uses expanding or sliding windows to simulate sequential forecasting. Random holdout or k-fold would leak future information into training.

Answer

Leave-one-out cross-validation

Answer

Random 80/20 train-test split

Answer

Stratified k-fold cross-validation

Question 14

A machine learning engineer is monitoring a production SageMaker endpoint using Amazon CloudWatch. They want to set up alarms for anomalous behavior. Which TWO CloudWatch metrics are MOST appropriate for detecting a sudden increase in request latency?

Accepted Answer

ModelLatency. ModelLatency directly measures request latency, and CPUUtilization can indicate resource saturation leading to latency increases.

Answer

5XXError

Answer

MemoryUtilization

Answer

Invocations

Question 15

A machine learning team is building a CI/CD pipeline to train and deploy models using Amazon SageMaker. They want to ensure that the deployment step only proceeds if the model evaluation metrics exceed a certain threshold. Which THREE components should the team include in the pipeline? (Choose THREE.)

Accepted Answer

The SageMaker Model Registry to approve and store the model after evaluation.. The SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.

Answer

An AWS Lambda function for manual approval.

Answer

An AWS CodeBuild project to compile the model artifacts.