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200+ Free AWS Machine Learning Specialty Practice Questions

Pass your AWS Certified Machine Learning – Specialty (MLS-C01) exam on the first try — instant access, no signup required.

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~55-65% Pass Rate
200+ Questions
100% Free

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Questions by Category

Aws-Ml-Modeling71 questions
Aws-Ml-Exploratory-Data-Analysis51 questions
Aws-Ml-Data-Engineering40 questions
Aws-Ml-Implementation-Operations38 questions
2026 Statistics

Key Facts: AWS Machine Learning Specialty Exam

~55-65%

Estimated Pass Rate

Industry estimate

750/1000

Passing Score

AWS

100-150 hrs

Study Time

Recommended

2+ years

AWS Experience

Recommended

65

Total Questions

50 scored + 15 unscored

$300

Exam Fee

AWS

The AWS Machine Learning Specialty (MLS-C01) requires a scaled score of 750/1000 to pass. The exam has 65 questions (50 scored + 15 unscored) in 180 minutes. Domain 3 (Modeling) is the largest at 36%, followed by Domain 2 (EDA) at 24%, Domain 1 (Data Engineering) at 20%, and Domain 4 (ML Ops) at 20%. AWS recommends 2+ years of hands-on ML experience. The exam fee is $300.

About the AWS Machine Learning Specialty Exam

The AWS Certified Machine Learning – Specialty (MLS-C01) validates your ability to build, train, tune, and deploy machine learning models on AWS. It is designed for data scientists and developers who perform development or data science roles. The exam covers data engineering, exploratory data analysis, modeling, and ML implementation and operations using SageMaker, Kinesis, Glue, and other AWS ML services.

Questions

65 scored questions

Time Limit

3 hours

Passing Score

750/1000

Exam Fee

$300 (Amazon Web Services (AWS))

AWS Machine Learning Specialty Exam Content Outline

20%

Data Engineering

S3, EFS, Kinesis, Firehose, Glue, EMR, data transformation, and data lakes

24%

Exploratory Data Analysis

Data cleaning, feature engineering, normalization, visualization, and labeling

36%

Modeling

Algorithm selection, XGBoost, deep learning, hyperparameter tuning, and model evaluation

20%

ML Implementation & Operations

SageMaker deployment, monitoring, security, cost optimization, and application services

How to Pass the AWS Machine Learning Specialty Exam

What You Need to Know

  • Passing score: 750/1000
  • Exam length: 65 questions
  • Time limit: 3 hours
  • Exam fee: $300

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

AWS Machine Learning Specialty Study Tips from Top Performers

1Focus on Domain 3 (Modeling, 36%) — it's the largest domain; master SageMaker training, hyperparameter tuning, and model evaluation metrics
2Know when to use different SageMaker deployment options: Real-Time Inference (low latency), Batch Transform (offline), Serverless Inference (sporadic), Asynchronous Inference (large payloads)
3Understand data engineering patterns: Kinesis Data Streams vs Firehose, Glue ETL vs EMR, S3 data lake architecture with Lake Formation
4Master model evaluation metrics: Accuracy, Precision, Recall, F1, AUC-ROC, RMSE, MAE — know when each is appropriate
5Know the ML services: Comprehend (NLP), Rekognition (vision), Polly (TTS), Transcribe (STT), Translate, Lex (chatbots), Personalize (recommendations), Forecast
6Understand feature engineering techniques: normalization, one-hot encoding, tokenization, handling missing values, and dimensionality reduction
7Practice with 200+ practice questions and aim for 80%+ on practice exams before scheduling
8Review the AWS ML Well-Architected Lens and SageMaker Best Practices whitepapers

Frequently Asked Questions

What is the AWS Machine Learning Specialty pass rate?

The AWS Machine Learning Specialty (MLS-C01) exam has an estimated pass rate of 55-65%. AWS requires a scaled score of 750 out of 1000. The exam is considered challenging and designed for experienced ML practitioners. Candidates with 2+ years of hands-on ML experience on AWS and thorough preparation typically pass on their first attempt.

How many questions are on the AWS Machine Learning Specialty exam?

The MLS-C01 exam has 65 total questions: 50 scored questions and 15 unscored pretest questions. You have 180 minutes (3 hours) to complete the exam. Questions are either multiple choice (one correct answer) or multiple response (two or more correct answers). Approximately 60% of questions are scenario-based, presenting real-world ML challenges on AWS.

What are the four domains of the MLS-C01 exam?

The four exam domains are: Domain 1 – Data Engineering (20%): S3, Kinesis, Glue, EMR, data ingestion and transformation; Domain 2 – Exploratory Data Analysis (24%): Data cleaning, feature engineering, visualization, and labeling; Domain 3 – Modeling (36%): Algorithm selection, XGBoost, deep learning, SageMaker training, hyperparameter tuning, and model evaluation; Domain 4 – ML Implementation & Operations (20%): Deployment, monitoring, security, and cost optimization.

How long should I study for the AWS Machine Learning Specialty exam?

Most candidates study for 8-12 weeks, investing 100-150 hours total. AWS recommends 2+ years of hands-on experience developing, architecting, or running ML workloads in AWS. Key study areas: 1) SageMaker ecosystem (training, tuning, deployment, monitoring). 2) Data engineering services (Kinesis, Glue, EMR). 3) Deep learning frameworks (TensorFlow, PyTorch). 4) Practice with 200+ questions and hands-on labs.

What AWS services are most important for the MLS-C01 exam?

Core services tested heavily: SageMaker (training jobs, hyperparameter tuning, model deployment, endpoints, monitoring); Data Engineering (Kinesis for streaming, Glue for ETL, EMR for Spark, S3 for storage); Analytics (Athena, Redshift); Application Services (Comprehend, Rekognition, Polly, Transcribe, Translate, Personalize, Forecast); Security (IAM, KMS, VPC endpoints). Deep knowledge of SageMaker is essential as it appears across all domains.

What is the difference between SageMaker Training Jobs and Processing Jobs?

SageMaker Training Jobs are used to train ML models on your dataset using built-in or custom algorithms. They handle infrastructure provisioning, distributed training, and model artifact output. SageMaker Processing Jobs are used for data preprocessing, feature engineering, and model evaluation. Processing Jobs are ideal for running feature transformation code on large datasets before training or running model evaluation after training.

When should I use built-in algorithms versus custom containers in SageMaker?

Use SageMaker built-in algorithms (XGBoost, Linear Learner, DeepAR, etc.) when they meet your requirements — they are optimized for performance and cost. Use custom containers (bring your own container) when you need specific libraries, frameworks, or custom code that built-in algorithms don't support. Custom containers provide flexibility but require more setup and management.