Pass the New AWS ML Engineer Associate (MLA-C02)

1. A team trains SageMaker models on a 4 TB tabular dataset stored as gzip-compressed CSV in Amazon S3. Analysts also run frequent Amazon Athena queries that scan only 6 of the 80 columns. Which change to the storage format will MOST reduce both Athena scan cost and SageMaker data-loading time?

   • A. Convert the data to a single uncompressed JSON Lines file
   • B. Re-store the data as RecordIO-protobuf shards
   • C. Keep CSV but increase the gzip compression level
   • D. Convert the data to Apache Parquet with Snappy compression

   Answer: D. Parquet is a columnar format, so Athena reads only the 6 needed columns instead of scanning all 80, and Snappy-compressed Parquet is splittable for parallel reads. JSON Lines is row-oriented and verbose, increasing scan size. RecordIO-protobuf suits certain built-in algorithms but is not queryable by Athena and does not enable columnar pruning. Higher gzip levels do not enable column pruning, and a single gzip CSV is not splittable.

   Exam domain: Data Preparation for ML and AI

2. Clickstream events arrive continuously and must be delivered to Amazon S3 as Parquet, partitioned by event date, with no application code to manage and built-in buffering and format conversion. The team wants the simplest fully managed pipeline. Which AWS service should they use?

   • A. Amazon Kinesis Data Streams with a custom consumer running on Amazon EC2
   • B. AWS Glue streaming job writing micro-batches to S3
   • C. Amazon Data Firehose with record format conversion to Parquet and dynamic partitioning
   • D. Amazon MSK with a self-managed Kafka Connect S3 sink

   Answer: C. Amazon Data Firehose (formerly Kinesis Data Firehose) is fully managed, buffers records, can convert JSON to Parquet, and supports dynamic partitioning to S3 with no servers to run. Kinesis Data Streams plus a custom EC2 consumer requires you to build and operate the delivery and conversion logic. MSK with Kafka Connect is self-managed and operationally heavier. A Glue streaming job is more code and infrastructure than needed for straightforward delivery to S3.

   Exam domain: Data Preparation for ML and AI

3. A team wants a visual, low-code tool to profile a CSV dataset, identify missing values and outliers, and apply 250+ prebuilt cleaning and normalization transformations without writing Spark or pandas code. Which AWS service is designed for this?

   • A. AWS Lambda
   • B. Amazon EMR on EC2
   • C. Amazon Athena
   • D. AWS Glue DataBrew

   Answer: D. AWS Glue DataBrew is a visual data-preparation tool with hundreds of prebuilt transformations and built-in profiling for missing values and outliers, requiring no code. Athena queries data with SQL but does not provide a visual cleaning UI. EMR requires writing Spark code, and Lambda is general-purpose compute, not a data-prep tool.

   Exam domain: Data Preparation for ML and AI

4. A high-cardinality categorical column 'product_id' has 50,000 distinct values and will feed a gradient-boosted tree model. One-hot encoding would create 50,000 sparse columns. Which encoding approach is the MOST appropriate to control dimensionality while preserving predictive signal?

   • A. Label encoding that assigns arbitrary sequential integers used directly as the feature
   • B. Dropping the column entirely because it is high cardinality
   • C. Target (mean) encoding computed only from the training split
   • D. One-hot encoding with all 50,000 categories retained

   Answer: C. Target encoding replaces each category with a statistic (such as the mean of the target) and keeps the feature one-dimensional, which works well for very high-cardinality columns; computing it only on the training split avoids leakage. One-hot at 50,000 levels explodes dimensionality. Arbitrary integer label encoding imposes a false ordinal relationship that misleads many models. Dropping the column discards potentially useful signal.

   Exam domain: Data Preparation for ML and AI

5. A data scientist has a tabular dataset in Amazon S3 with a labeled target column and wants SageMaker to automatically explore feature preprocessing, candidate algorithms, and hyperparameters to produce a leaderboard of trained models with minimal code. Which SageMaker capability is the BEST fit?

   • A. Amazon SageMaker Debugger
   • B. Amazon SageMaker JumpStart foundation models
   • C. Amazon SageMaker Model Monitor
   • D. Amazon SageMaker Autopilot (AutoML)

   Answer: D. Autopilot (AutoML) automatically handles preprocessing, algorithm selection, and hyperparameter tuning on tabular data, producing a ranked leaderboard of candidate models. JumpStart provides pretrained foundation/proprietary models rather than AutoML over your tabular data. Debugger inspects training jobs for issues, and Model Monitor watches deployed endpoints for drift, neither of which builds candidate models.

   Exam domain: ML Model and Foundation Model Development

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