100% Free Amazon MLA-C01 Practice Test Questions

Question 1

A company wants to share data with a vendor in real time to improve the performance of thevendor's ML models. The vendor needs to ingest the data in a stream. The vendor will use only someof the columns from the streamed data.Which solution will meet these requirements?

A : Use AWS Data Exchange to stream the data to an Amazon S3 bucket. Use an Amazon AthenaCREATE TABLE AS SELECT (CTAS) query to define relevant columns.

B : Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for ApacheFlink as a consumer to extract relevant columns.

C : Create an Amazon S3 bucket. Configure the S3 bucket policy to allow the vendor to upload data tothe S3 bucket. Configure the S3 bucket policy to control which columns are shared.

D : Use AWS Lake Formation to ingest the data. Use the column-level filtering feature in LakeFormation to extract relevant columns.

Answer: B

Question 2

A company wants to build an anomaly detection ML model. The model will use large-scale tabulardata that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark,or other languages for ML.An ML engineer needs to transform and prepare the data for ML model training.Which solution will meet these requirements?

A : Prepare the data by using Amazon EMR Serverless applications that host Amazon SageMakerStudio notebooks.

B : Prepare the data by using the Amazon SageMaker Data Wrangler visual interface in AmazonSageMaker Canvas.

C : Run SQL queries from a JupyterLab space in Amazon SageMaker Studio. Process the data furtherby using pandas DataFrames.

D : Prepare the data by using a JupyterLab notebook in Amazon SageMaker Studio.

Answer: B

Question 3

A company regularly receives new training data from a vendor of an ML model. The vendor deliverscleaned and prepared data to the companys Amazon S3 bucket every 3“4 days.The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs torun the pipeline automatically when new data is uploaded to the S3 bucket.Which solution will meet these requirements with the LEAST operational effort?

A : Create an S3 lifecycle rule to transfer the data to the SageMaker AI training instance and initiatetraining.

B : Create an AWS Lambda function that scans the S3 bucket and initiates the pipeline when new datais uploaded.

C : Create an Amazon EventBridge rule that matches S3 upload events and configures the SageMakerpipeline as the target.

D : Use Amazon Managed Workflows for Apache Airflow (MWAA) to orchestrate the pipeline whennew data is uploaded.

Answer: C

Question 4

A company wants to share data with a vendor in real time to improve the performance of thevendor's ML models. The vendor needs to ingest the data in a stream. The vendor will use only someof the columns from the streamed data.Which solution will meet these requirements?

A : Use AWS Data Exchange to stream the data to an Amazon S3 bucket. Use an Amazon AthenaCREATE TABLE AS SELECT (CTAS) query to define relevant columns.

B : Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for ApacheFlink as a consumer to extract relevant columns.

C : Create an Amazon S3 bucket. Configure the S3 bucket policy to allow the vendor to upload data tothe S3 bucket. Configure the S3 bucket policy to control which columns are shared.

D : Use AWS Lake Formation to ingest the data. Use the column-level filtering feature in LakeFormation to extract relevant columns.

Answer: B

Question 5

A travel company has trained hundreds of geographic data models to answer customer questions byusing Amazon SageMaker AI. Each model uses its own inferencing endpoint, which has become anoperational challenge for the company.The company wants to consolidate the models' inferencing endpoints to reduce operationaloverhead.Which solution will meet these requirements?

A : Use SageMaker AI multi-model endpoints. Deploy a single endpoint.

B : Use SageMaker AI multi-container endpoints. Deploy a single endpoint.

C : Use Amazon SageMaker Studio. Deploy a single-model endpoint.

D : Use inference pipelines in SageMaker AI to combine tasks from hundreds of models to 15 models.

Answer: A

Free Practice Amazon MLA-C01 Exam Questions 2026