Free Practice Questions for AWS Certified Machine Learning - Specialty (MLS-C01) Certification

- 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]).

- 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.

- 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).

- 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).

- 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).

- 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).

- 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.

- 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.

- 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

- 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).

- 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.

- 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.

- 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.

- AWS Identity and Access Management (IAM) - S3 bucket policies - Security groups - VPCs - Encryption and anonymization

- 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.