MLA-C01 Practice Questions 1 — 50 Questions

Question 1

A team uses SageMaker Clarify to monitor bias drift in production. They schedule weekly analysis. After a month, Clarify reports a significant increase in a bias metric. What should the team do first?

Accepted Answer

Review the analysis report to understand which feature and segment contributed to the drift.. Reviewing the report helps understand which feature and segment contributed to the drift. Option A is premature without understanding the cause. Option B is ignoring the issue. Option C does not address the drift source.

Answer

Disable the bias monitor because the metric may be noisy.

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Immediately retrain the model with a balanced dataset.

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Increase the frequency of analysis to daily.

Question 2

A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?

Accepted Answer

Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling. The most cost-effective configuration is using a Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling. The ml.c5.4xlarge instance provides sufficient compute (16 vCPUs) to achieve under 10 ms inference latency and handle 1000 requests per second. Auto scaling ensures the endpoint adapts to traffic, minimizing cost while meeting demand. Although MME is typically designed for multiple models, it can also host a single model; the cost advantage here comes from the right-sized instance and scaling, not from the multi-model feature. A single ml.c5.xlarge instance (option C) lacks the vCPU capacity to handle 1000 req/s at sub-10ms latency. Deploying separate endpoints on ml.m5.large instances (option D) would require many instances, increasing cost. Batch Transform (option A) is for batch inference, not real-time.

Answer

Use SageMaker Batch Transform with multiple instances

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Deploy on a single ml.c5.xlarge instance with a real-time endpoint

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Deploy separate real-time endpoints for each model on ml.m5.large instances

Question 3

A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)

Accepted Answer

Package the model artifacts into a tar.gz file.. SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.

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Upload the training data to an S3 bucket.

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Register the model in the SageMaker Model Registry.

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Set up a SageMaker Notebook instance.

Question 4

A machine learning team is preparing data for a binary classification model. The target variable has a severe class imbalance (95% negative, 5% positive). The team wants to train a model that maximizes recall on the positive class while keeping training time manageable. Which approach should they use?

Accepted Answer

Use SMOTE to generate synthetic samples for the positive class. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples of the minority class, increasing its representation without simply duplicating data. This often improves recall without drastically increasing training time compared to other methods.

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Use class weights inversely proportional to class frequencies

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Oversample the positive class by simply duplicating existing records

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Undersample the majority class to match the minority class size

Question 5

A media company uses SageMaker to host a real-time video recommendation model. The model is deployed on a single ml.c5.xlarge endpoint. During a major live event, traffic surges to 10 times the normal load, and the endpoint becomes unresponsive, causing high latency and errors. The team had set up an Application Auto Scaling target tracking policy based on CPU utilization with a target of 70%. However, scaling did not trigger quickly enough. After the event, the team reviews CloudWatch metrics and notices that CPU utilization never exceeded 70% during the surge, but memory utilization peaked at 95%. The model is memory-bound. The team wants to ensure the endpoint scales automatically before performance degrades during future events. What should the team do?

Accepted Answer

Change the target tracking metric to memory utilization and set a target of 70%. The model is memory-bound, and the current CPU-based target tracking policy failed to trigger scaling since CPU utilization never exceeded 70% during the surge. By switching to a memory utilization metric with a target of 70%, scaling will activate based on the actual resource constraint (memory), preventing performance degradation before the endpoint becomes unresponsive.

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Increase the target CPU utilization to 90% so that scaling triggers at higher load

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Change the endpoint instance type to ml.c5.4xlarge to provide more memory per instance

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Create a scheduled scaling policy to add instances during the known event time

Question 6

A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?

Accepted Answer

Set up Amazon SageMaker Model Monitor with data quality monitoring.. Amazon SageMaker Model Monitor with data quality monitoring is the correct solution because it automatically detects deviations in the input data distribution compared to a baseline, which directly addresses the problem of model accuracy degradation due to data drift. It continuously monitors the statistical properties of inference requests and alerts when drift is detected, enabling proactive retraining.

Answer

Configure AWS Config rules to check the model accuracy metric.

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Use AWS CloudTrail to monitor changes to the model's S3 bucket.

Answer

Enable Amazon CloudWatch Logs on the endpoint and set alarms on inference latency.

Question 7

A machine learning engineer is deploying a model using a SageMaker endpoint and needs to ensure that the model artifacts are encrypted at rest using a customer-managed KMS key. Which configuration should they set?

Accepted Answer

Set the KMS key when creating the model using the CreateModel API. The `CreateModel` API in SageMaker accepts a `ModelKmsKeyId` parameter that specifies a customer-managed KMS key for encrypting the model artifacts at rest. This key is used when SageMaker copies the artifacts from S3 to the inference instance's Amazon EBS volume, ensuring encryption at rest. The other options either apply to different resources or do not control the encryption of the model artifacts themselves.

Answer

Set the KMS key in the endpoint configuration's ProductionVariant

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Enable default encryption on the S3 bucket containing the model artifacts

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Use SageMaker Studio's KMS integration

Question 8

A company wants to reduce costs for a production SageMaker endpoint that has predictable traffic patterns. They have purchased a Savings Plan. What additional step can they take to further optimize costs while maintaining performance?

Accepted Answer

Use SageMaker Inference Recommender to right-size the endpoint. SageMaker Inference Recommender provides instance type and configuration recommendations to right-size endpoints, balancing cost and performance. It is the appropriate tool for cost optimization beyond a Savings Plan.

Answer

Reduce the number of instances to one, regardless of load

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Switch from real-time to batch inference

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Disable auto-scaling

Question 9

A machine learning engineer is developing a text classification model using Amazon SageMaker. The dataset consists of 1 million customer reviews, with labels indicating sentiment (positive, negative, neutral). The engineer uses a pre-trained BERT model from the Hugging Face Model Hub and fine-tunes it on the dataset using SageMaker's Hugging Face estimator with a ml.p3.2xlarge instance. After 2 hours of training, the training job fails with a 'ResourceExhaustedError: CUDA out of memory' error. The error occurs during the forward pass of the first epoch. The engineer confirms that the batch size is set to 32, the maximum sequence length is 512 tokens, and the dataset is stored in a S3 bucket in the same AWS region. The engineer needs to complete fine-tuning without increasing instance costs. Which course of action should the engineer take?

Accepted Answer

Reduce the batch size to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.. Reducing the batch size to 8 directly lowers GPU memory usage per forward pass, and enabling gradient accumulation with 4 steps allows the model to simulate the original effective batch size of 32 (8 × 4 = 32) without increasing memory footprint. This approach resolves the CUDA out-of-memory error while keeping the same instance type (ml.p3.2xlarge) and without incurring additional costs.

Answer

Enable SageMaker Managed Spot Training to reduce costs and use the savings to upgrade to a ml.p3.8xlarge instance.

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Switch to a CPU-based instance like ml.c5.2xlarge to avoid GPU memory constraints.

Answer

Reduce the maximum sequence length to 128 tokens to lower memory consumption.

Question 10

A team is deploying a TensorFlow model on a SageMaker real-time endpoint with automatic scaling. They set the scaling policy to target an average CPU utilization of 50%. However, during traffic spikes, the endpoint experiences high latency and 503 errors. The instance type is ml.c5.large. What should the team do to resolve this while minimizing cost?

Accepted Answer

Add a scaling policy based on the number of concurrent requests per instance. Scaling based on CPU utilization alone is often insufficient for inference workloads where latency is the primary concern. By adding a scaling policy based on the number of concurrent requests per instance, the team can proactively scale out before CPU saturation occurs, reducing latency and eliminating 503 errors. SageMaker's automatic scaling supports multiple target tracking metrics, and using concurrent requests per instance aligns more closely with the actual demand on the model serving container.

Answer

Pre-warm the endpoint by keeping a fixed number of additional instances

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Increase the scale-in cooldown period to avoid frequent downsizing

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Change the instance type to a larger one like ml.c5.xlarge to handle the spikes

Question 11

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

Answer

Amazon S3 Select

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Amazon EMR

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AWS Lambda

Question 12

A team is collaborating on a machine learning project and needs to ensure that data used for training is consistent across experiments. The team wants to version datasets, track data lineage, and be able to reproduce past experiments. The team uses SageMaker for model training. Which combination of services and features should the team use?

Accepted Answer

Use SageMaker with AWS Lake Formation to manage data access, version datasets in S3, and use SageMaker Experiments to track training jobs.. It combines AWS Lake Formation for fine-grained data access control and governance, S3 versioning for dataset versioning, and SageMaker Experiments to track training jobs and lineage. This trio directly addresses the need for consistent data across experiments, versioning, lineage tracking, and reproducibility in SageMaker.

Answer

Use SageMaker Pipelines to automate training and store datasets in S3 with versioning enabled.

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Store datasets in Amazon DynamoDB and use Amazon Athena to query specific versions.

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Use S3 versioning to store all dataset versions and AWS Glue Data Catalog to track schema changes.

Question 13

Refer to the exhibit. A data engineer runs an AWS Glue ETL job with the following script portion. The job fails with an error: 'An error occurred while calling o113.pyWriteDynamicFrame. No such file or directory'. What is the most likely cause?

Accepted Answer

The output S3 path is missing a trailing slash. The error 'No such file or directory' when calling `pyWriteDynamicFrame` typically occurs because AWS Glue expects the output S3 path to end with a trailing slash to denote a directory. Without it, Glue may interpret the path as a file name rather than a directory, leading to a failure when attempting to write the Parquet files. Adding a trailing slash (e.g., `s3://bucket/output/`) resolves the issue.

Answer

The output format 'parquet' is not supported by Glue

Answer

The input partition path is incorrect because it includes the partition key

Answer

The schema contains a column with a reserved name

Question 14

An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?

Accepted Answer

AWS Glue. AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.

Answer

Amazon EMR

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Amazon Athena

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Amazon Redshift

Question 15

A data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?

Accepted Answer

Store the underlying data in a columnar format like Parquet. Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.

Answer

Create an AWS Glue DataBrew recipe to transform the data

Answer

Add each partition manually using ALTER TABLE ADD PARTITION

Question 16

A company wants to allow a SageMaker model in one AWS account to be accessed by a different AWS account for inference. They need to maintain security and compliance. Which approach meets the requirement?

Accepted Answer

Attach a resource-based policy to the SageMaker endpoint that grants the other account's IAM role invoke permissions. Cross-account access can be achieved by using resource-based policies on the SageMaker model or endpoint, combined with appropriate IAM roles in the consuming account.

Answer

Use AWS PrivateLink to expose the SageMaker endpoint privately and grant access via security groups

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Create an IAM role in the source account and share the role ARN with the target account

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Share the model artifacts via an S3 bucket with cross-account bucket policies and let the other account deploy independently

Question 17

A company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?

Accepted Answer

Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback. SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.

Answer

Manually update the endpoint to point to the previous model version

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Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic

Answer

Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer

Question 18

After deploying a model to a SageMaker endpoint, the operations team notices high inference latency. They suspect it is due to insufficient instance capacity. Which first step should they take to diagnose the issue?

Accepted Answer

Review Amazon CloudWatch metrics for the endpoint, such as CPUUtilization and Invocations.. Amazon CloudWatch metrics for a SageMaker endpoint, such as `CPUUtilization`, `MemoryUtilization`, and `Invocations`, directly indicate whether the instance is overloaded. High `CPUUtilization` combined with a high `Invocations` count and increased latency strongly suggests insufficient instance capacity. This is the standard first diagnostic step for capacity-related performance issues.

Answer

Check AWS CloudTrail logs for API errors.

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Use Amazon SageMaker Debugger to analyze inference performance.

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Retrain the model with more training data.

Question 19

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

Accepted Answer

Relationalize transform. The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.

Answer

Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 20

A company wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?

Accepted Answer

Amazon SageMaker Pipelines. Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.

Answer

AWS CodePipeline

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AWS Glue Workflows

Answer

AWS Step Functions