This exam was retired by the vendor on March 31, 2026.
MLS-C01 can no longer be scheduled. The practice material below remains available for reference and for learners studying the underlying skills. See MLA-C01 practice questions on Courseiva
Amazon Web Services certification practice
Practise AWS Certified Machine Learning Specialty MLS-C01 across all 4 official exam domains — Data Engineering, Machine Learning Implementation and Operations, Modeling, Exploratory Data Analysis — with original exam-style questions, full explanations, and wrong-answer analysis. AWS certification exams usually test architecture trade-offs: service selection, resilience, cost, security, networking and operations. Courseiva helps you practise those decisions through original scenario-based questions.
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Recommended: 25-question diagnostic
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Historical facts for the AWS Certified Machine Learning Specialty MLS-C01 exam, which the vendor has retired.
Exam cost (historical)
$300 USD / ~£240
Duration (historical)
180 minutes
Question count
65 questions
Passing score
750 / 1000
The AWS Certified Machine Learning – Specialty (MLS-C01) validates expertise in building, training, tuning, and deploying machine learning models on AWS. The exam tests knowledge of the complete ML lifecycle — from data engineering and exploratory analysis to model training with Amazon SageMaker, hyperparameter optimisation, and production deployment. Candidates must also demonstrate understanding of ML algorithms, feature engineering, evaluation metrics, and AWS AI services such as Rekognition, Comprehend, and Forecast. It is suited to data scientists and ML engineers who design production ML systems on AWS.
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Everything on Courseiva for MLS-C01 is free — no subscription, no trial, no email wall.
Courseiva provides free AWS Certified Machine Learning Specialty MLS-C01 (MLS-C01) practice questions with original exam-style questions, detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics. Courseiva is an independent platform and is not affiliated with Amazon Web Services.
Official prerequisite
None formal; AWS recommends 1–2 years of ML/deep learning experience plus at least 1 year of AWS experience.
Recommended experience
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, or scikit-learn); hands-on SageMaker experience; understanding of statistics and algorithms.
1–2 years of practical ML experience and at least 1 year working with AWS services.
Should you skip this exam?
Those without ML fundamentals — build machine learning foundations before attempting this Specialty exam.
Courseiva study tip
Deep-dive into SageMaker training, deployment, and monitoring; understand algorithm selection trade-offs; practise feature engineering and evaluation metrics questions heavily.
UK salary range
£70,000–£110,000
US salary range
$140,000–$190,000
Job roles that cite MLS-C01
What certification to take next
MLS-C01 was retired by the vendor and can no longer be scheduled or booked. See MLA-C01 practice questions on Courseiva for the current path.
The MLS-C01 exam costs $300 USD (approximately £240 GBP).
You need a scaled score of 750 out of 1000 to pass.
There are 65 scored questions (multiple choice and multiple response) with 180 minutes allowed.
It is rated as hard. The exam requires both ML theory knowledge and practical AWS implementation skills, particularly around SageMaker. Candidates without strong ML foundations often struggle.
The exam covers data engineering (S3, Glue, Kinesis), exploratory data analysis, modelling (SageMaker algorithms, hyperparameter tuning, training jobs), and deployment and monitoring (endpoints, A/B testing, Model Monitor).
The certification is valid for 3 years and can be renewed through AWS recertification before expiry.
It is designed for data scientists and ML engineers who build and deploy ML models on AWS, particularly those with hands-on SageMaker experience.
See how MLS-C01 stacks up against related certifications.
Courseiva Exam Intelligence
Data confidence: BuildingDerived from Courseiva's continuously updated practice dataset: 1,672 exam-style questions. Updated 2 July 2026.
Question difficulty mix
Average time per question: 98s
4
Exam domains
official blueprint domains covered by the bank
1,672
Questions
exam-style questions in the MLS-C01 bank
Practice modes
Choose a mode that matches your current study phase. Use topic practice early on and mock exams closer to your exam date.
All MLS-C01 Questions
Browse the full question bank organised by domain and difficulty.
Practice Test
Start a 20-question session with a full explanation after each answer.
Mock Exam
Simulate the real exam with a timed, full-length question set.
Quick 25
A 25-question warm-up — ideal for a short daily study session.
Flashcards
Reinforce key terms, commands, and protocol facts with flashcards.
Domain Practice
Focus exclusively on one exam domain to target your weakest area.
50-Question Set
Mid-length practice set covering all domains.
Exam guide
Start with a short practice session, review each missed answer, then return to the topics that caused mistakes.
AWS Certified Machine Learning Specialty MLS-C01 spans 4 official exam domains (Data Engineering, Machine Learning Implementation and Operations, Modeling, and more). Questions test whether you can apply each objective in a realistic scenario, not just recall a definition.
Related practice questions
Use these internal links to move from the exam guide into focused topic practice pages.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice directory
Every practice set is a unique selection of questions — numbered tests, domain-focused sessions, and topic-specific quizzes.
Showing 8 of 92 tests. See all 92 MLS-C01 practice tests →
Official blueprint
Target weak areas by practising questions from a single domain. Each domain page shows all questions, difficulty breakdown, and a focused practice session.
Practise MLS-C01 questions in this domain →
Practise MLS-C01 questions in this domain →
Practise MLS-C01 questions in this domain →
Practise MLS-C01 questions in this domain →
Deep-dive questions
The most-searched MLS-C01 questions — each with a detailed explanation, worked example, and full answer breakdown.
Learn the concepts
15 book-style chapters cover every MLS-C01 objective in plain English. Read the theory here, then test it with practice questions.
Chapter 1
Machine Learning Overview and Core Concepts
12 min read
Chapter 2
Data Engineering Foundations for Machine Learning
12 min read
Chapter 3
Data Preparation and Transformation
12 min read
Chapter 4
AWS Data Stores and Ingestion Pipelines
12 min read
Chapter 5
Exploratory Data Analysis and Visualization
12 min read
Chapter 6
Feature Engineering, Encoding, and Selection
12 min read
Chapter 7
Data Quality, Missing Values, and Bias Detection
12 min read
Chapter 8
Modeling Fundamentals: Algorithms and Problem Types
12 min read
Free study guide
The Courseiva MLS-C01 study guide covers exam format, the official domain blueprint with weights, a structured study plan, and domain-specific tips for each section. Use it alongside practice questions to build both conceptual depth and exam confidence.
Sample questions
Original exam-style questions — not copied from any exam or dump site. Open a question for the full answer breakdown.
A data scientist needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to creat…
A data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this…
Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?
A company uses Amazon SageMaker to deploy a model for real-time predictions. The model is updated weekly. The company wants to ensure that the new model version is gradually rolled…
A data scientist needs to create a SageMaker notebook instance with access to a private S3 bucket. The bucket uses SSE-KMS encryption. Which additional configuration is required?
A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expect…
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Ways to study MLS-C01