MLA-C01 Practice Test 40 — 15 Questions

Question 1

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?

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

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.

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Reserved instances

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Dedicated hosts

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On-demand instances

Question 2

A company wants to automate the deployment of a SageMaker model into production whenever a new model version is approved in the Model Registry. Which service can be used to trigger the deployment pipeline?

Accepted Answer

Amazon CloudWatch Events (EventBridge). Amazon EventBridge (formerly CloudWatch Events) can detect state changes in SageMaker Model Registry, such as when a model version is approved. It can then trigger a target like AWS CodePipeline or a Lambda function to automate the deployment pipeline, making it the correct choice for event-driven automation of model deployment.

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AWS Lambda

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Amazon S3 Events

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Amazon SNS

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AWS Config

Question 3

A company uses SageMaker to train a model. The training job is failing with an error "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has reached the limit for number of training instances. The 'ResourceLimitExceeded' error in SageMaker indicates that your AWS account has reached a service quota for a specific resource, such as the number of training instances. SageMaker enforces per-region limits on the number of ml.* instances that can be used concurrently for training jobs, and exceeding this quota triggers the error. This is a common issue when scaling up training without requesting a limit increase.

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The model artifact is too large to upload

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Invalid hyperparameters

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The training data size exceeds the available instance storage

Question 4

A data scientist wants to version control trained models and manage approvals for deployment. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry.. SageMaker Model Registry is the correct feature because it is specifically designed for versioning trained models, tracking their metadata (e.g., training job, metrics), and managing approval workflows for deployment stages (e.g., Pending, Approved, Rejected). This directly addresses the data scientist's need to version control models and manage deployment approvals.

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SageMaker Experiments.

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SageMaker Feature Store.

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SageMaker Ground Truth.

Question 5

Refer to the exhibit. A team configured a SageMaker Model Monitor schedule for data quality. The baseline was created from a training dataset. After running for a day, the monitoring results show frequent violations. What is the most likely cause?

Accepted Answer

The baseline was created from a dataset that does not represent production data.. SageMaker Model Monitor compares production data against a baseline statistics and constraints file. If the baseline was created from a training dataset that does not reflect the actual distribution, patterns, or schema of production data, the monitor will flag frequent violations. This is the most common cause of false-positive alerts in data quality monitoring.

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The environment variable max_runtime_in_seconds is too low.

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The schedule runs too often (every hour), causing overload.

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The monitoring output destination is incorrect.

Question 6

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

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Principal Component Analysis (PCA) dimensionality reduction

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Standard scaling of numerical features

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One-hot encoding of categorical variables

Question 7

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Accepted Answer

AWS CDK (Cloud Development Kit). AWS CDK (A) is correct because it is an infrastructure-as-code framework that synthesizes CloudFormation templates, and it provides L2 constructs such as CfnDomain, CfnUserProfile, and CfnApp for managing SageMaker Studio domains, user profiles, and associated resources declaratively. Terraform by HashiCorp (D) is correct because it is a third-party IaC tool with dedicated AWS provider resources like aws_sagemaker_domain, aws_sagemaker_user_profile, and aws_sagemaker_app that let teams manage the same SageMaker Studio resources in HCL state files. AWS CloudFormation (E) is correct because it is AWS's native IaC service and directly supports the AWS::SageMaker::Domain, AWS::SageMaker::UserProfile, and AWS::SageMaker::App resource types for provisioning and updating these resources. The SageMaker Python SDK (B) is not an infrastructure-as-code service; it is a high-level library for training, deploying, and interacting with models and endpoints, not for declaratively managing Studio domain infrastructure. Boto3 (C) is the AWS SDK for Python and can call SageMaker APIs imperatively, but it is not an infrastructure-as-code tool with declarative templates or state management, so it does not fit the IaC requirement.

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SageMaker Python SDK

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Boto3

Question 8

A team needs to deploy a PyTorch model that uses custom CUDA kernels. They want to use NVIDIA Triton Inference Server on SageMaker for high-performance serving. Which SageMaker configuration is required to use Triton?

Accepted Answer

Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR. SageMaker provides a pre-built Triton Inference Server container in Amazon ECR that is optimized for high-performance serving of models, including those with custom CUDA kernels. This container eliminates the need to build a custom image from scratch, ensuring compatibility with SageMaker's deployment infrastructure and reducing operational overhead.

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Create a custom container from scratch with Triton and deploy on SageMaker

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Use a Multi-Model Endpoint with Triton

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Attach an Amazon Elastic Inference accelerator to the endpoint

Question 9

A model deployed on a SageMaker endpoint is returning predictions. The team wants to log all predictions to an S3 bucket for auditing. What is the most efficient way to achieve this?

Accepted Answer

Enable SageMaker endpoint data capture to the S3 bucket.. SageMaker endpoint data capture is the native, most efficient way to log predictions to S3 because it automatically captures input payloads and output predictions for all requests to the endpoint, storing them directly in the specified S3 bucket without any custom code or additional infrastructure. This feature is designed specifically for auditing and monitoring, requiring only a DataCaptureConfig to be set on the endpoint.

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Configure CloudWatch Logs to export to S3.

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Modify the inference code to write logs to S3.

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Use Amazon Kinesis Data Firehose to stream predictions to S3.

Question 10

Which TWO SageMaker Pipelines steps are essential for automating a complete ML workflow from data processing to model deployment? (Choose 2.)

Accepted Answer

A ProcessingStep to run data preprocessing and feature engineering.. Option B (ProcessingStep) is correct because it is the pipeline step that runs a SageMaker Processing job to execute data preprocessing, feature engineering, and dataset splitting, which is the required starting point for an end-to-end workflow from raw data to a deployable model. Option D (CreateModelStep or RegisterModelStep) is correct because it produces the SageMaker model artifact needed to complete the workflow: CreateModelStep packages the model for deployment, while RegisterModelStep records it in the Model Registry for governed deployment, thereby covering the model deployment end of the pipeline. Option A (TuningStep) is not essential here because hyperparameter tuning is an optional optimization stage, not a required step for a complete processing-to-deployment workflow. Option C (TransformStep) is not essential because batch inference on training data is an evaluation/inference activity, not a required stage for automating training and deployment. Option E (ConditionStep) is not essential because conditional branching on data quality is an optional control-flow enhancement rather than a mandatory step in a basic end-to-end pipeline.

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A TuningStep for hyperparameter tuning.

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A TransformStep for batch inference on the training data.

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A ConditionStep to decide whether to train a model based on data quality.

Question 11

A machine learning engineer observes that a SageMaker training job fails with the error shown in the exhibit. What is the most likely cause of the failure?

Accepted Answer

The SageMaker execution role does not have an IAM policy that grants read access to the S3 bucket containing the training data.. The error shown in the exhibit is a standard SageMaker access-denied error, which occurs when the SageMaker execution role lacks the necessary IAM permissions to read the training data from the S3 bucket. SageMaker uses the execution role's IAM policy to determine access to S3 resources; without a policy granting s3:GetObject (and optionally s3:ListBucket) on the bucket and objects, the training job fails at the data-loading stage.

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The training data is stored in an unsupported format like Parquet.

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The training job is using an incorrect AWS Region for the S3 bucket.

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The VPC configuration prevents the training job from reaching the S3 bucket.

Question 12

A company uses AWS Glue ETL jobs to clean and transform data from S3 before training. The data contains a column with 40% missing values. The column is normally distributed. Which imputation strategy should the data engineer use?

Accepted Answer

Impute with mean. For normally distributed data, imputing with the mean is the standard approach because it is efficient and unbiased. Option A (mode) is for categorical data and inappropriate for numeric data. Option C (median) is robust but less efficient for normal data. Option D (drop rows) would lose 40% of data, which is excessive.

Answer

Impute with mode

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Impute with median

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Drop rows with missing values

Question 13

A company uses Amazon SageMaker Ground Truth to create a labeled dataset. They want to monitor the accuracy of human labelers during the labeling process. Which metric should they track?

Accepted Answer

Accuracy against blinded ground truth. Ground Truth supports a built-in quality control mechanism where a percentage of tasks are sent to multiple workers, and one worker's answer is treated as the 'ground truth' (blinded to the others). By comparing each labeler's output against this blinded ground truth, the labeling job computes per-worker accuracy metrics, which is the direct measure of labeler quality. This is the metric designed specifically to monitor human labeler accuracy during the job.

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Labeling job cost

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Number of tasks completed

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Task acceptance rate

Question 14

A data science team deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?

Accepted Answer

ml.p3.2xlarge. (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.

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ml.m5.2xlarge

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ml.p4d.24xlarge

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ml.c5.2xlarge

Question 15

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.

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

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

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