Free AWS Certified Machine Learning Engineer Associate Exam

Question 1

A company collects IoT sensor data from 10,000 devices, with each device sending a JSON payload every second. The data must be stored in Amazon S3 for ML training, and the team uses Amazon Athena for downstream queries. The team is concerned about the small files problem: millions of tiny files in S3 that degrade Athena query performance because of excessive file listing and opening overhead.

Which solution addresses the small files problem with minimal operational overhead?

A. Use an AWS Lambda function triggered by each device event to write each JSON payload as an individual object in Amazon S3.

B. Use Amazon S3 event notifications to trigger a compaction Lambda function that periodically merges small files into larger ones.

C. Store the sensor data in Amazon DynamoDB instead of Amazon S3 to avoid the small files problem entirely.

D. Use Amazon Data Firehose with buffering configured (for example, a 128 MB buffer size or a 300-second buffer interval) and enable Apache Parquet format conversion.

Question 2

An ML team is preparing a dataset for a sentiment analysis model. The dataset contains raw customer review text that needs to be preprocessed before feature extraction and model training. The team needs to apply appropriate text preprocessing techniques to normalize and structure the raw text.

Which text preprocessing techniques should the team apply? (Select any two options.)

A. Tokenization: splitting raw text into individual tokens (words or subwords) as a fundamental preprocessing step.

B. One-hot encode the entire review text to convert each review into a binary feature vector.

C. Apply min-max scaling to the text data to normalize values between zero and one.

D. Remove stop words and apply lowercasing to normalize the text before vectorization.

E. Bin the review text into predefined numeric categories based on text length.

Question 3

A financial services company needs to validate the quality of a newly ingested dataset before using it for model retraining. The company wants automated data quality checks that flag issues such as missing values exceeding 5%, duplicate rows, and schema drift compared to the expected schema. The validation must be integrated into the existing AWS Glue ETL pipeline.

Which solution should the company implement?

A. Use Amazon SageMaker Model Monitor to detect data quality issues in the ingested dataset before training.

B. Use Amazon Macie to scan the dataset for data quality anomalies and schema violations.

C. Write custom Python validation scripts in an AWS Lambda function triggered by S3 upload events.

D. Use AWS Glue Data Quality rules defined in Data Quality Definition Language (DQDL) to specify completeness, uniqueness, and schema conformity checks, and integrate them into the Glue ETL pipeline.

Question 4

A retail company is building an ML pipeline that learns from three kinds of data: product photos uploaded by suppliers as JPEG files, recorded customer support calls stored as WAV files, and live video from cameras on its warehouse inspection lines. Training jobs must be able to find every asset for a given product ID and capture date, and the camera feeds must be kept for 30 days so that clips can be pulled later for labeling.

Which ingestion and storage design should the ML engineer implement?

A. Store each image and audio file as a Base64-encoded attribute in an Amazon DynamoDB table keyed by product ID, and write the camera video frames to the same table.

B. Send the images, the audio files and the camera video through a single Amazon Kinesis Data Streams stream, and have a consumer application write every record to Amazon S3.

C. Land the images and audio in Amazon S3 under modality- and date-partitioned prefixes (for example, raw/images/dt=2026-10-01/ and raw/audio/dt=2026-10-01/) with a JSON metadata record per object that links it to the product ID and capture time, and ingest the camera feeds with Amazon Kinesis Video Streams using a 30-day retention period, exporting the clips needed for labeling to Amazon S3.

D. Use Amazon Data Firehose to stream the live camera video ...