Problem
I want to specialize in Machine Learning. Which certification is recommended?
Solution
In this tip, we will show some material for the AWS Machine Learning Engineer Certification. This exam will teach how to pass the AWS exam MLA-C01.
What is the MLA-C01 exam?
The AWS Machine Learning Engineer Certification (MLA-C01) is an exam that measures your knowledge of Machine Learning in the Amazon ecosystem. This exam measures your knowledge to develop Machine Learning solutions in AWS.
Is this exam difficult?
This exam is difficult. If you already have some years of experience in Machine Learning in AWS, the exam should not be very difficult. However, if you lack experience, the exam will be particularly challenging.
What is the passing score to pass the exam?
The minimum score to pass is 700/1000.
What books are recommended for this exam?
The following books will provide you with valuable information to pass the exam.
- AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam (Sybex Study Guide)
- AWS CERTIFIED MACHINE LEARNING ENGINEER – ASSOCIATE | EXAM CODE: MLA-C01 | FAST TRACK PREPARATION, 6 PRACTICE TESTS, 500+ FOUNDATIONAL QUESTIONS, 500+ EXAM TIPS, 500+ CAUTION ALERTS
- AWS Certified Machine Learning Engineer – Associate (MLA-C01) – Service Summary Sheets: Get answer to all W questions for AWS Services covered in AWS … on latest MLA-C01 (AWS Service Summary Cards)
- Amazon AWS Certified Machine Learning – Associate (MLA-C01) Exam Guide
- AWS Certified: Machine Learning Engineer – Associate (MLA-C01) Question Bank & Study Materials: Master the Exam with Detailed Explanations & Online Discussion … Question Bank & Study Materials)
- A textbook for passing the AWS Certified Machine Learning Engineer Associate exam taught by an AWS All certified engineer: 30 practice questions and Cheat … Compatible with MLA C01 (Japanese Edition)
Can you please share some links to study for the MLA-C01 exam?
Yes. The following links can be useful for the exam:
Domain 1: Preparing Data for Machine Learning
Task 1.1: Collect and store data
- File types and data ingestion methods (e.g., Parquet, JSON, CSV, ORC, Avro, RecordIO).
- Main AWS storage options like Amazon S3, EFS, and FSx for ONTAP.
- Real-time data ingestion services (e.g., Amazon Kinesis, Apache Flink, Apache Kafka).
- Storage solutions in AWS, along with their use cases and tradeoffs.
Task 1.2: Process data and perform feature engineering
- Methods for cleaning and transforming data (e.g., handling missing values, removing duplicates, detecting outliers).
- Techniques used for feature engineering (e.g., normalization, scaling, binning, log transformation).
- Data encoding methods (e.g., one-hot, binary, label encoding, tokenization).
- Tools for exploring, visualizing, and transforming data (e.g., SageMaker Data Wrangler, AWS Glue, Glue DataBrew).
- Services for streaming data transformation (e.g., AWS Lambda, Spark).
- Data labeling and annotation solutions.
Task 1.3: Validate data quality and prepare for modeling
- Metrics for detecting bias before training (numeric, text, image data).
- Ways to address class imbalance (e.g., resampling, generating synthetic data).
- Data encryption methods.
- Techniques for classification, anonymization, and masking of sensitive data.
- Compliance considerations (e.g., PII, PHI, residency requirements).
Domain 2: Developing ML Models
Task 2.1: Select a modeling strategy
- Capabilities and applications of different ML algorithms.
- AWS AI services like Translate, Transcribe, Rekognition, and Bedrock.
- Considering interpretability when choosing algorithms.
- Algorithms in SageMaker and when to use them.
Task 2.2: Train and improve models
- Core components of training (epochs, batch size, steps).
- Techniques to speed up training (e.g., distributed training, early stopping).
- Factors that affect model size.
- Approaches to enhance performance.
- Regularization methods (e.g., dropout, weight decay, L1, L2).
- Hyperparameter optimization approaches (e.g., random search, Bayesian optimization).
- How different hyperparameters influence outcomes.
- Approaches to bring externally built models into SageMaker.
Task 2.3: Evaluate model performance
- Model assessment methods and metrics (e.g., confusion matrix, F1 score, accuracy, recall, RMSE, ROC, AUC).
- Techniques for establishing baselines.
- Detecting overfitting and underfitting.
- Metrics using SageMaker Clarify for data and model insights.
- Understanding convergence problems.
Domain 3: Deploying and Orchestrating ML Workflows
Task 3.1: Choose deployment infrastructure to fit architecture and requirements
- Best practices for deployment (e.g., versioning, rollbacks).
- AWS services for deploying ML models (e.g., SageMaker).
- Options for serving models (batch and real-time).
- Provisioning computing resources for both testing and production (CPU, GPU).
- Endpoint requirements (serverless, async, real-time, batch inference).
- Container selection (pre-built or custom).
- Running optimized models on edge devices (SageMaker Neo).
Task 3.2: Build and script infrastructure
- Differences between provisioned and on-demand resources.
- Scaling policies comparison.
- Tradeoffs when using infrastructure as code (CloudFormation, AWS CDK).
- Key containerization concepts and AWS container tools.
- SageMaker auto scaling policies for endpoints.
Task 3.3: Automate orchestration and CI/CD pipelines
- Quotas and capabilities of CodePipeline, CodeBuild, and CodeDeploy.
- How to connect data ingestion processes with orchestration tools.
- Basics of version control (e.g., Git).
- Applying CI/CD concepts to ML pipelines.
- Strategies for deployment and rollback (blue/green, canary, linear).
- Integration of repositories with pipelines.
Domain 4: Monitoring, Maintaining, and Securing ML Solutions
Task 4.1: Track model inference
- Understanding and detecting model drift.
- Approaches for monitoring both data quality and model accuracy.
- Design principles for monitoring ML systems.
Task 4.2: Monitor and control infrastructure and costs
- Important infrastructure metrics (e.g., availability, utilization, scalability, fault tolerance).
- Monitoring tools and observability solutions (e.g., AWS X-Ray, CloudWatch Insights).
- Using CloudTrail for logging and retraining triggers.
- Differences in instance types and performance tradeoffs.
- Cost management tools (e.g., Cost Explorer, Trusted Advisor).
- Approaches to track and allocate costs (e.g., tagging).
Task 4.3: Secure ML resources
- IAM concepts: roles, policies, and groups.
- Security and compliance features in SageMaker.
- Network access controls for ML workloads.
- Best practices for securing CI/CD pipelines.
Next Steps
For more information about this exam, refer to the following links.
- AWS Certified Machine Learning Engineer – Associate MLA-C01
- AWS Certified Machine Learning Engineer – Associate (MLA-C01) Exam Guide
- AWS Certified Machine Learning Engineer – Associate
- Everything You Need to Know About AWS Machine Learning Associate Certification (MLA-C01)
- AWS Certified Machine Learning Engineer – Associate Practice Test | MLA-C01 Exam Preparation
- More articles for certification preparation
Daniel Calbimonte is a Microsoft Most Valuable Professional, Microsoft Certified Trainer and Microsoft Certified IT Professional for SQL Server. He is an accomplished SSIS author, teacher at IT Academies and has over 10 years of experience as a QE and developer for SQL Server related software. He has worked for the government, oil companies, web sites, magazines and universities around the world. Daniel also regularly speaks at SQL Servers conferences and blogs.
- MSSQLTips Awards: Author of the Year Contender – 2015-2018, 2022, 2023 | Champion (100+ tips) – 2018
