AWSAssociateRetired · 28 September 2026
AWS Certified Machine Learning Engineer – Associate
MLA-C01
Build, deploy and operationalise ML workloads on AWS. MLA-C02 replaces this exam from late September 2026.
This exam is retired — no longer supported
This exam was retired on 28 September 2026 and can no longer be sat, so CertifyCloudx no longer offers practice questions or mock exams for it. The exam details and the official syllabus below are kept for reference.
Withdrawn — CertifyCloudx does not prepare candidates for this exam any more. What follows is the official exam guide, kept for reference.
What's on the exam
4 domains · 12 task statements, straight from the official exam guide (as of 2026-09-14).
- 1.1Ingest and store data
- Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
- How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
- How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
- AWS storage options, including use cases and tradeoffs
- Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
- Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
- Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
- Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
- Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
- Making initial storage decisions based on cost, performance, and data structure
- 1.2Transform data and perform feature engineering
- Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
- Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
- Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
- Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
- Services that transform streaming data (for example, AWS Lambda, Spark)
- Data annotation and labeling services that create high-quality labeled datasets
- Transforming data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
- Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
- Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)
- 1.3Ensure data integrity and prepare data for modeling
- Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
- Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
- Techniques to encrypt data
- Data classification, anonymization, and masking
- Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)
- Validating data quality (for example, by using AWS Glue DataBrew and AWS Glue Data Quality)
- Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
- Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
- Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)
Outline reproduced from the vendor's public exam guide for study reference.Official guide