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.
A data science team needs to store large-scale training datasets that will be accessed by multiple SageMaker training jobs running in different Availability Zones. The data must be durable, highly available, and accessible via standard file system interfaces. Which storage solution should they use?
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.
Sample AWS Machine Learning Specialty Practice Questions
Try these sample questions to test your AWS Machine Learning Specialty exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.
1A data science team needs to store large-scale training datasets that will be accessed by multiple SageMaker training jobs running in different Availability Zones. The data must be durable, highly available, and accessible via standard file system interfaces. Which storage solution should they use?
2A company needs to ingest real-time streaming data from IoT sensors for immediate ML inference. The data volume varies significantly throughout the day. Which AWS service combination is most cost-effective for this use case?
3Which S3 storage class is most appropriate for ML training datasets that are accessed frequently for the first 30 days, then rarely accessed but must remain immediately available for occasional retraining?
4A data engineering team needs to perform complex ETL operations on terabytes of data before ML training. They require support for Apache Spark and the ability to use custom libraries. Which service should they choose?
5What is the primary advantage of using AWS Glue Data Catalog compared to a self-managed Hive Metastore?
6A team needs to create a data lake with multiple data sources and enforce column-level security for different analyst groups. Which AWS services should they use?
7For a batch ML inference job that processes hundreds of GB of data nightly and writes results to S3, which compute option is most cost-effective while maintaining reliability?
8A company needs to process clickstream data in real-time for immediate personalization. The data arrives at variable rates up to 100,000 records per second during peak hours. Which architecture is most appropriate?
9When designing a data ingestion pipeline for ML, which factor should most influence the choice between batch and streaming processing?
10Which data format is generally most efficient for storing large-scale structured ML training data in S3 for queries with Athena?
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
Data Engineering
S3, EFS, Kinesis, Firehose, Glue, EMR, data transformation, and data lakes
Exploratory Data Analysis
Data cleaning, feature engineering, normalization, visualization, and labeling
Modeling
Algorithm selection, XGBoost, deep learning, hyperparameter tuning, and model evaluation
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
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.