MLA-C01 Practice Exam 55 — 20 Questions

Question 1

Which TWO options are recommended best practices for monitoring model performance in production on SageMaker? (Choose 2.)

Accepted Answer

Use SageMaker Clarify for bias monitoring and feature importance drift.. SageMaker Clarify is a recommended best practice for monitoring model performance because it provides automated bias detection and feature importance drift analysis, helping to identify when model predictions become unfair or when the relationships between features and predictions change over time. This is critical for maintaining model integrity and compliance in production.

Answer

Retrain the model daily based on recent data without evaluation.

Answer

Set up a CloudWatch alarm on the endpoint's Invocations metric.

Answer

Manually compare prediction distributions weekly.

Question 2

A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?

Accepted Answer

SageMaker Endpoint Autoscaling. SageMaker Endpoint Autoscaling is the correct feature because it automatically adjusts the number of instances behind a SageMaker hosted endpoint based on a target metric (e.g., requests per minute, CPU utilization) using Application Auto Scaling. This allows the endpoint to handle varying request volumes without manual intervention, ensuring cost efficiency and performance.

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Answer

SageMaker Pipelines

Question 3

A team is using SageMaker Clarify to detect bias drift in a deployed model's predictions. They run weekly bias monitoring jobs. The team wants to be notified when the bias metric for a sensitive feature exceeds a threshold. What is the most efficient method to achieve this?

Accepted Answer

After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric. SageMaker Clarify bias monitoring jobs output a JSON report to S3 but do not natively publish CloudWatch metrics. By using a custom Lambda to parse the report and publish a custom CloudWatch metric, you can then create a CloudWatch alarm that triggers notifications when the bias metric exceeds a threshold. This is the most efficient automated method because it leverages CloudWatch's native alarm and notification capabilities without manual intervention.

Answer

Configure the Clarify monitoring job to send results to an SNS topic directly

Answer

Manually review the bias report in SageMaker Studio each week

Answer

Use SageMaker Model Monitor - Bias Drift Monitor which automatically creates CloudWatch metrics

Question 4

A company uses SageMaker Studio for collaborative ML development. The security team requires that all SageMaker Studio notebooks run within a VPC and cannot access the public internet. Which configuration should the administrator set?

Accepted Answer

Enable VPC-only mode for the SageMaker Studio domain. Enabling VPC-only mode for the SageMaker Studio domain ensures that all Studio notebooks and apps are launched within the specified VPC and cannot access the public internet. This mode enforces that all network traffic, including internet-bound traffic, is routed through the VPC, and it blocks direct internet access by default, meeting the security team's requirement.

Answer

Use SageMaker notebook instances instead of Studio

Answer

Apply an SCP that denies internet access for all IAM users

Answer

Set the SageMaker Studio domain to use a public subnet with a NAT Gateway

Question 5

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

Accepted Answer

Undersample the majority class. Undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.

Answer

Remove outliers from the minority class

Answer

Apply PCA to reduce dimensionality

Answer

Use stratified splitting for train/test sets

Question 6

A company needs to secure a SageMaker notebook instance that contains sensitive data. Which THREE of the following are effective security measures? (Select THREE.)

Accepted Answer

Use IAM policies to restrict who can access the notebook instance.. IAM policies are the primary mechanism for controlling access to AWS resources, including SageMaker notebook instances, ensuring only authorized users can interact with the instance. Disabling direct internet access and using a VPC with a NAT gateway is a network security best practice that prevents exposure to the public internet while allowing controlled outbound traffic, which is critical for sensitive data. Encrypting the EBS volume with AWS KMS protects data at rest. AWS CloudTrail is useful for auditing API calls, but it is a logging and monitoring tool, not a direct security measure that prevents unauthorized access or data exposure.

Answer

Attach a lifecycle configuration that runs a script to download data from a public S3 bucket.

Answer

Enable AWS CloudTrail to log all notebook API calls.

Question 7

A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?

Accepted Answer

Deploy the model using Amazon SageMaker with a TensorFlow inference container.. Amazon SageMaker provides a fully managed TensorFlow inference container that directly supports TensorFlow SavedModel format, enabling deployment without any custom infrastructure management. This minimizes operational overhead compared to self-managed options like ECS or Lambda, as SageMaker handles scaling, load balancing, and model updates automatically.

Answer

Deploy the model on Amazon ECS using a custom Docker image.

Answer

Deploy the model as an AWS Lambda function with the TensorFlow runtime.

Answer

Deploy the model using Amazon SageMaker Studio.

Question 8

A data scientist is working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)

Accepted Answer

Principal Component Analysis (PCA) to reduce dimensionality of numerical features.. Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.

Answer

Domain-specific feature engineering based on business rules.

Answer

One-hot encoding all categorical features.

Question 9

An organization wants to schedule a retraining pipeline to run every Sunday night. Which AWS service should they use to trigger the pipeline on a schedule?

Accepted Answer

Amazon EventBridge. Amazon EventBridge is the correct choice because it provides a scheduled event source using cron or rate expressions to trigger target services at specified times, such as every Sunday night. It can directly invoke an AWS Step Functions state machine or a Lambda function to start the retraining pipeline, making it the native scheduling service for event-driven workflows in AWS.

Answer

AWS Lambda

Answer

AWS Step Functions

Answer

Amazon SQS

Question 10

A data scientist is preparing a dataset for a binary classification model to predict customer churn. The dataset contains a timestamp column 'signup_date' that is not relevant for the prediction. What is the most appropriate action to handle this column?

Accepted Answer

Drop the 'signup_date' column from the dataset.. The 'signup_date' column is explicitly stated as not relevant for the prediction. In binary classification for customer churn, including an irrelevant timestamp can introduce noise, increase dimensionality, and potentially cause overfitting. Dropping the column is the most appropriate action to maintain model simplicity and focus on predictive features.

Answer

Apply one-hot encoding to the year, month, and day components.

Answer

Convert the timestamp to a numeric feature (e.g., days since signup) and include it.

Answer

Use leave-one-out encoding based on the target variable.

Question 11

A data science team uses SageMaker Studio with a VPC-only mode. They need to access a private S3 bucket in the same VPC to read training data. The SageMaker Studio domain is configured with VPC-only mode. Which configuration ensures the Studio notebook can access the S3 bucket without traversing the public internet?

Accepted Answer

Create an S3 Gateway Endpoint in the VPC and attach a bucket policy that allows access from the VPC. An S3 Gateway Endpoint provides a private, VPC-only route to S3 without traversing the public internet. By attaching a bucket policy that restricts access to the VPC endpoint, the SageMaker Studio domain (configured with VPC-only mode) can securely read training data from the private S3 bucket using AWS PrivateLink.

Answer

Set the S3 bucket as public and restrict access by source IP

Answer

Configure a NAT Gateway in the public subnet and route Studio traffic through it

Answer

Use a SageMaker execution role with a policy that allows s3:GetObject from any network

Question 12

A company is using Amazon SageMaker Data Wrangler to prepare a dataset for training. They have created a data flow with multiple transforms. Which TWO actions can they take to operationalize the data preparation pipeline for production? (Choose 2)

Accepted Answer

Export the data flow to a SageMaker Processing job. SageMaker Data Wrangler can export a data flow directly to a SageMaker Processing job. This allows the data preparation logic to be run as a managed, scalable batch job in production, integrating seamlessly with the SageMaker ecosystem for repeatable and scheduled processing.

Answer

Create a scheduled SageMaker Pipeline by directly converting the Data Wrangler flow

Answer

Convert the data flow directly to Amazon SageMaker Autopilot

Answer

Deploy the data flow as a real-time inference endpoint

Question 13

A company wants to implement cost monitoring and optimization for SageMaker endpoints. Which TWO actions should they take? (Select TWO)

Accepted Answer

Use SageMaker Savings Plans to get discounted rates in exchange for a commitment. SageMaker Savings Plans offer discounted compute rates (up to 64% off on-demand) in exchange for a 1- or 3-year commitment to a consistent amount of compute usage (measured in dollars per hour), directly reducing endpoint costs. This is a cost optimization mechanism, not a monitoring one, and aligns with the goal of reducing spend on SageMaker endpoints.

Answer

Manually scale instances based on historical patterns

Answer

Enable detailed billing reports to track endpoint costs

Answer

Use SageMaker Model Monitor to reduce endpoint costs

Question 14

A machine learning engineer wants to automatically trigger a retraining pipeline whenever new training data arrives in an S3 bucket. The pipeline uses SageMaker Pipelines. Which AWS service should be used to detect the S3 event and start the pipeline?

Accepted Answer

Amazon EventBridge. Amazon EventBridge can be configured to listen for S3 events (e.g., PutObject) and then invoke a Lambda function that starts the SageMaker Pipeline execution. Step Functions could orchestrate the pipeline but is not needed to trigger on S3 events. SageMaker Pipelines does not natively listen to S3 events. CloudWatch Events is the older name for EventBridge.

Answer

SageMaker Pipelines native S3 trigger

Answer

AWS Step Functions

Answer

Amazon CloudWatch Logs

Question 15

A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?

Accepted Answer

Serverless inference. SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.

Answer

Real-time endpoint with auto-scaling

Answer

Batch transform

Answer

Asynchronous inference

Question 16

A machine learning engineer needs to reduce costs when training a large model on SageMaker. They are willing to accept potential interruptions and have checkpointing enabled. Which instance purchasing option should they use?

Accepted Answer

Spot instances. Spot instances offer significant cost savings (up to 60-90%) compared to on-demand, but can be reclaimed by AWS with a 2-minute notice. Checkpointing allows resuming training from the last saved state, making spot instances suitable.

Answer

Reserved instances

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Dedicated hosts

Answer

On-demand instances

Question 17

A company is preparing data for a time-series forecasting model. The data is collected from IoT sensors at irregular intervals. Which TWO steps are necessary to prepare the data? (Choose 2.)

Accepted Answer

Resample the data to a fixed frequency. Time-series forecasting models require data at consistent time intervals to capture temporal patterns and seasonality. Resampling the irregular IoT sensor data to a fixed frequency (e.g., every 5 minutes) creates a uniform time index, which is essential for algorithms like ARIMA, Prophet, or LSTM. This step ensures the model can learn from a structured sequence rather than being confused by variable time gaps.

Answer

Normalize the data to a 0-1 range

Answer

Remove outlier data points

Answer

Encode categorical features

Question 18

A company uses SageMaker Experiments to track training runs. They want to compare different hyperparameter configurations and identify the best run. Which SageMaker Experiments component should they use to organize related runs?

Accepted Answer

Experiment + Trial

Answer

Trial

Answer

Experiment

Answer

Trial Component

Question 19

A data engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?

Accepted Answer

Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.. SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.

Answer

Write the script to process data in chunks and write intermediate results to local ephemeral storage.

Answer

Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.

Answer

Reduce the number of instances to one and increase the volume size for swap space.

Question 20

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

Accepted Answer

Data quality monitor. The Data quality monitor in SageMaker Model Monitor is specifically designed to detect violations in the input data schema, such as changes in the number of features, feature types, or missing values, by comparing incoming data against a baseline computed from the training dataset. Since the issue is a structural change in the schema (number of features), the Data quality monitor is the correct choice.

Answer

Bias drift monitor

Answer

Feature attribution drift monitor

Answer

Model quality monitor