MLA-C01 Practice Test 2 — 10 Questions

Question 1

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.

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Amazon S3 Select

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Amazon EMR

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AWS Lambda

Question 2

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.

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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 3

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.

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The output format 'parquet' is not supported by Glue

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

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The schema contains a column with a reserved name

Question 4

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.

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Amazon EMR

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Amazon Athena

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Amazon Redshift

Question 5

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

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Add each partition manually using ALTER TABLE ADD PARTITION

Question 6

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.

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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 7

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.

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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

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

Question 8

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.

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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 9

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.

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Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 10

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.

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AWS CodePipeline

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AWS Glue Workflows

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AWS Step Functions