Free Practice Questions for AWS Certified Machine Learning - Specialty (MLS-C01) Certification
Study with 360 exam-style practice questions designed to help you prepare for the AWS Certified Machine Learning - Specialty (MLS-C01).
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Exam Details
Key information about AWS Certified Machine Learning - Specialty (MLS-C01)
- Multiple choice
Multiple choice, Multiple response
750 out of 1,000
Experience performing basic hyperparameter optimization and with ML/deep learning frameworks.
Individuals in an AI/ML development or data science role with 2+ years of experience developing, architecting, and running ML/deep learning workloads in the AWS Cloud.
50 scored questions, 15 unscored questions
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Data Engineering
Subdomain 1.1: Create data repositories for ML
• Identify data sources (for example, content and location, primary sources such as user data). • Determine storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]).
Subdomain 1.2: Identify and implement a data ingestion solution
• Identify data job styles and job types (for example, batch load, streaming). • Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads). • Amazon Kinesis • Amazon Data Firehose • Amazon EMR • AWS Glue • Amazon Managed Service for Apache Flink • Schedule jobs.
Subdomain 1.3: Identify and implement a data transformation solution
• Transform data in transit (ETL, AWS Glue, Amazon EMR, AWS Batch). • Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive).
Domain 2: Exploratory Data Analysis
Subdomain 2.1: Sanitize and prepare data for modeling
• Identify and handle missing data, corrupt data, and stop words. • Format, normalize, augment, and scale data. • Determine whether there is sufficient labeled data. • Identify mitigation strategies. • Use data labelling tools (for example, Amazon Mechanical Turk).
Subdomain 2.2: Perform feature engineering
• Identify and extract features from datasets, including from data sources such as text, speech, images, and public datasets. • Analyze and evaluate feature engineering concepts (for example, binning, tokenization, outliers, synthetic features, one-hot encoding, reducing dimensionality of data).
Subdomain 2.3: Analyze and visualize data for ML
• Create graphs (for example, scatter plots, time series, histograms, box plots). • Interpret descriptive statistics (for example, correlation, summary statistics, p-value). • Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot, cluster size).
Domain 3: Modeling
Subdomain 3.1: Frame business problems as ML problems
• Determine when to use and when not to use ML. • Know the difference between supervised and unsupervised learning. • Select from among classification, regression, forecasting, clustering, recommendation, and foundation models.
Subdomain 3.2: Select the appropriate model(s) for a given ML problem
• XGBoost, logistic regression, k-means, linear regression, decision trees, random forests, RNN, CNN, ensemble, transfer learning, and large language models (LLMs) • Express the intuition behind models.
Subdomain 3.3: Train ML models
• Split data between training and validation (for example, cross validation). • Understand optimization techniques for ML training (for example, gradient descent, loss functions, convergence). • Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed). • Choose appropriate compute platforms (Spark or non-Spark). • Update and retrain models. • Batch or real-time/online
Subdomain 3.4: Perform hyperparameter optimization
• Perform regularization. • Dropout • L1/L2 • Perform cross-validation. • Initialize models. • Understand neural network architecture (layers and nodes), learning rate, and activation functions. • Understand tree-based models (number of trees, number of levels). • Understand linear models (learning rate).
Subdomain 3.5: Evaluate ML models
• Avoid overfitting or underfitting. • Detect and handle bias and variance. • Evaluate metrics (for example, area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score). • Interpret confusion matrices. • Perform offline and online model evaluation (A/B testing). • Compare models by using metrics (for example, time to train a model, quality of model, engineering costs). • Perform cross-validation.
Domain 4: Machine Learning Implementation and Operations
Subdomain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
• Log and monitor AWS environments. • AWS CloudTrail and Amazon CloudWatch • Build error monitoring solutions. • Deploy to multiple AWS Regions and multiple Availability Zones. • Create AMIs and golden images. • Create Docker containers. • Deploy Auto Scaling groups. • Rightsize resources (for example, instances, Provisioned IOPS, volumes). • Perform load balancing. • Follow AWS best practices.
Subdomain 4.2: Recommend and implement the appropriate ML services and features for a given problem
• ML on AWS (application services), for example: • Amazon Polly • Amazon Lex • Amazon Transcribe • Amazon Q • Understand AWS service quotas. • Determine when to build custom models and when to use Amazon SageMaker built-in algorithms. • Understand AWS infrastructure (for example, instance types) and cost considerations. • Use Spot Instances to train deep learning models by using AWS Batch.
Subdomain 4.3: Apply basic AWS security practices to ML solutions
• AWS Identity and Access Management (IAM) • S3 bucket policies • Security groups • VPCs • Encryption and anonymization
Subdomain 4.4: Deploy and operationalize ML solutions
• Expose endpoints and interact with them. • Understand ML models. • Perform A/B testing. • Retrain pipelines. • Debug and troubleshoot ML models. • Detect and mitigate drops in performance. • Monitor performance of the model.
Techniques & products