Ingest and store data.
6 practice questions under this official exam objective (MLA-C01) — each with the community-verified answer, a full option-by-option explanation and instant feedback.
AWS Lake Formation Aggregating Fraud-Detection Training Data from S3 and On-Premises MySQL
The fraud-detection training set spans Amazon S3 (transaction logs, customer profiles) and tables in an on-premises MySQL database, and the question a
How to Label Employee-Only Image Data With Maximum Accuracy?
Building a supervised image model requires accurate ground-truth labels, and for confidential employee-only images the labeling must happen inside a c
How to Access FSx for ONTAP Training Data in SageMaker?
Amazon FSx for NetApp ONTAP volumes can be mounted directly to a SageMaker instance in the same VPC, giving ML code NFS access to 6 TB of image traini
Best AWS Tool to Merge and Clean Datasets for ML?
Amazon SageMaker Data Wrangler is the purpose-built tool for importing multiple datasets, joining them into one data frame, and applying built-in clea
Querying Transaction Dates in Centrally Stored S3 CSV Objects with Athena CTAS and No Data Movement
Thousands of existing and newly uploaded CSV objects sit in a central S3 bucket with the same column structure, including a transaction date column, a
Building Batch Data Ingestion from S3 with AWS Glue and Model Deployment Pipelines in SageMaker Studio
An ML engineer needs to create both data ingestion pipelines and ML model deployment pipelines, and all the raw data already sits in Amazon S3 buckets