Applied AI cert-prep add-on

AWS Certified Machine Learning - Specialty Exam Prep Add-On

Convert AI Engineering Mastery into an AWS Machine Learning Specialty (MLS-C01) ramp in 75 hours.

$197 add-on75h additional studyTarget exam: AWS Certified Machine Learning - Specialty (MLS-C01)Exam fee: $300

What the add-on ships

++Four domain reviews (Data Engineering, Exploratory Data Analysis, Modeling, ML Implementation & Operations) mapped to the official AWS MLS-C01 exam guide
++Three full-length 65-question mock exams scored against the AWS scaled-score model
++SageMaker hands-on labs covering training jobs, hyperparameter tuning, endpoint deployment, and pipeline orchestration
++Exam-day scenario walkthroughs for the AWS-specific question patterns
++Direct link to the official AWS exam guide as the canonical reference

Built on the parent course

++AI Engineering Mastery 18-module curriculum covering RAG, fine-tuning, evals, agents, observability, cost engineering
++Production-grade AI engineering patterns from the parent course that map directly to AWS exam scenarios

Parent course: ai engineering mastery

Best for

++ML Engineers consolidating one to two years of AWS ML experience into a senior credential
++AI Engineers crossing into MLOps roles where AWS-specific credential signal is requested
++Solutions Architects with an ML-heavy customer portfolio

Practice surface

++Three 65-question mock exams in AWS scenario format
++SageMaker labs runnable on the AWS free tier
++Targeted weak-area question drilling per AWS domain weighting

Buy the add-on

$197 on top of the ai engineering mastery parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

AWS Certified Machine Learning - Specialty (MLS-C01) is AWS's senior-tier ML credential. The exam covers data engineering for ML, exploratory data analysis and modeling, ML deployment and operations on AWS, and ML implementation across the AWS service catalog. Exam is 180 minutes, 65 questions, scaled passing score of approximately 750/1000. The credential targets practitioners with one to two years of hands-on AWS ML experience.

Domain reviews

Data Engineering

20%

How data lands in AWS for ML: ingestion, storage, ETL, feature engineering, data validation.

++Storage choices: S3, EBS, EFS, FSx for Lustre
++Ingestion: Kinesis Data Streams, Kinesis Data Firehose, AWS Glue
++ETL: AWS Glue, EMR with Spark, AWS Lambda
++Feature engineering: encoding categoricals, scaling, embeddings, time-window features
++Data validation: AWS Glue Data Quality, SageMaker Model Monitor data quality checks

Primary sources:

Exploratory Data Analysis and Modeling

24%

Algorithm selection, hyperparameter tuning, and modeling decisions in AWS.

++Algorithm families: linear, tree-based (XGBoost), deep learning
++SageMaker built-in algorithms: XGBoost, Linear Learner, BlazingText, Object2Vec, etc.
++Hyperparameter tuning: SageMaker Automatic Model Tuning, Bayesian vs random search
++Evaluation metrics: accuracy, precision, recall, F1, ROC-AUC, RMSE, MAE
++Imbalanced data techniques: oversampling, class weighting, threshold adjustment

Primary sources:

SageMaker built-in algorithms reference

ML Implementation and Operations

28%

Deploying, monitoring, and operating ML in AWS production.

++Deployment patterns: SageMaker real-time, batch transform, async, serverless
++Multi-model endpoints and multi-variant endpoints
++SageMaker Pipelines: orchestrating training, evaluation, registration
++Monitoring: SageMaker Model Monitor for data drift and model drift
++Security: VPC endpoints, KMS encryption, IAM least-privilege, network isolation

Primary sources:

Machine Learning Implementation Across AWS Services

28%

Choosing the right AWS service for the use case and integrating across services.

++AI services: Rekognition, Transcribe, Translate, Comprehend, Textract, Forecast, Personalize
++AWS Bedrock: foundation models, model evaluation, agent framework
++Glue, EMR, Athena, Redshift for upstream data
++Choosing between custom training (SageMaker) and pre-built AI services (Comprehend, Rekognition)
++Hybrid architectures: SageMaker plus AWS AI services plus Bedrock

Primary sources:

Practice scenarios (7)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 7 · Data Engineeringfree preview

An ML team needs to land 10 GB/hour of clickstream events into S3 in near real time, partitioned by event date. They want the lowest operational overhead. Which combination best fits?

A. Kinesis Data Streams plus a manually-managed Lambda consumer that writes to S3
B. Kinesis Data Firehose with S3 destination, partitioning by event date enabled in the Firehose configuration
C. Direct S3 PUT from each client browser
D. EC2 fleet running a custom log forwarder to S3

Answer: B

Kinesis Data Firehose is the managed delivery stream that lands data into S3 with partitioning, buffering, and format conversion handled by AWS. Kinesis Data Streams plus custom Lambda requires more operational work. Browser PUT and EC2 fleets are anti-patterns for high-volume managed ingestion.

Reference: AWS Kinesis Data Firehose documentation

Unlock the rest

6 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $197 one-time, lifetime access.

Exam-day strategy

++Identify which exam domain a question targets (Data Engineering vs Modeling vs Implementation vs Cross-Service). The right answer often resolves by domain alignment.
++AWS scenario questions usually have one option that names a managed service designed for the exact use case. Eliminate options that require custom code for problems AWS sells a service for.
++Watch for distractors that confuse training-time vs serving-time services (Feature Store vs Model Monitor, Pipelines vs endpoints).
++On algorithm questions, prefer the SageMaker built-in algorithm where one fits the data shape; custom Docker containers are rarely the canonical answer.
++Pace at roughly 2 minutes 45 seconds per question. 180 minutes for 65 questions is generous; flag and return on multi-service architecture questions.

Additional resources

Sources

Official AWS Certified Machine Learning - Specialty (MLS-C01) exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Four domain reviews (Data Engineering, Exploratory Data Analysis, Modeling, ML Implementation & Operations) mapped to the official AWS MLS-C01 exam guide

++Three full-length 65-question mock exams scored against the AWS scaled-score model

++SageMaker hands-on labs covering training jobs, hyperparameter tuning, endpoint deployment, and pipeline orchestration

++Exam-day scenario walkthroughs for the AWS-specific question patterns

++Direct link to the official AWS exam guide as the canonical reference

Exam summary

Domain reviews

Data Engineering

20%

How data lands in AWS for ML: ingestion, storage, ETL, feature engineering, data validation.

++Storage choices: S3, EBS, EFS, FSx for Lustre
++Ingestion: Kinesis Data Streams, Kinesis Data Firehose, AWS Glue
++ETL: AWS Glue, EMR with Spark, AWS Lambda
++Feature engineering: encoding categoricals, scaling, embeddings, time-window features
++Data validation: AWS Glue Data Quality, SageMaker Model Monitor data quality checks

Primary sources:

Exploratory Data Analysis and Modeling

24%

Algorithm selection, hyperparameter tuning, and modeling decisions in AWS.

++Algorithm families: linear, tree-based (XGBoost), deep learning
++SageMaker built-in algorithms: XGBoost, Linear Learner, BlazingText, Object2Vec, etc.
++Hyperparameter tuning: SageMaker Automatic Model Tuning, Bayesian vs random search
++Evaluation metrics: accuracy, precision, recall, F1, ROC-AUC, RMSE, MAE
++Imbalanced data techniques: oversampling, class weighting, threshold adjustment

Primary sources:

SageMaker built-in algorithms reference

ML Implementation and Operations

28%

Deploying, monitoring, and operating ML in AWS production.

++Deployment patterns: SageMaker real-time, batch transform, async, serverless
++Multi-model endpoints and multi-variant endpoints
++SageMaker Pipelines: orchestrating training, evaluation, registration
++Monitoring: SageMaker Model Monitor for data drift and model drift
++Security: VPC endpoints, KMS encryption, IAM least-privilege, network isolation

Primary sources:

Machine Learning Implementation Across AWS Services

28%

Choosing the right AWS service for the use case and integrating across services.

++AI services: Rekognition, Transcribe, Translate, Comprehend, Textract, Forecast, Personalize
++AWS Bedrock: foundation models, model evaluation, agent framework
++Glue, EMR, Athena, Redshift for upstream data
++Choosing between custom training (SageMaker) and pre-built AI services (Comprehend, Rekognition)
++Hybrid architectures: SageMaker plus AWS AI services plus Bedrock

Primary sources:

Practice scenarios (7)

Preview scenario 1 of 7 · Data Engineeringfree preview

An ML team needs to land 10 GB/hour of clickstream events into S3 in near real time, partitioned by event date. They want the lowest operational overhead. Which combination best fits?

A. Kinesis Data Streams plus a manually-managed Lambda consumer that writes to S3
B. Kinesis Data Firehose with S3 destination, partitioning by event date enabled in the Firehose configuration
C. Direct S3 PUT from each client browser
D. EC2 fleet running a custom log forwarder to S3

Answer: B

Reference: AWS Kinesis Data Firehose documentation

Unlock the rest

6 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $197 one-time, lifetime access.

Exam-day strategy

++Identify which exam domain a question targets (Data Engineering vs Modeling vs Implementation vs Cross-Service). The right answer often resolves by domain alignment.

++AWS scenario questions usually have one option that names a managed service designed for the exact use case. Eliminate options that require custom code for problems AWS sells a service for.

++Watch for distractors that confuse training-time vs serving-time services (Feature Store vs Model Monitor, Pipelines vs endpoints).

++On algorithm questions, prefer the SageMaker built-in algorithm where one fits the data shape; custom Docker containers are rarely the canonical answer.

++Pace at roughly 2 minutes 45 seconds per question. 180 minutes for 65 questions is generous; flag and return on multi-service architecture questions.