AWSSpecialty2026 Updated

AWS Machine Learning Specialty MLS-C01 Exam Guide 2026: Everything You Need to Pass

Updated May 1, 202612 min readWritten by Certsqill experts

Quick facts — MLS-C01

Exam cost

$300 USD

Questions

65 items

Time limit

180 minutes

Passing score

750 / 1000

Valid for

3 years

Testing

Pearson VUE

Who this exam is for

The AWS Machine Learning Specialty MLS-C01 certification is designed for professionals who work with or want to work with AWS technologies in a professional capacity. It is taken by cloud engineers, DevOps practitioners, IT administrators, and technical professionals looking to validate their expertise.

You do not need extensive prior experience to attempt it, but you will benefit from hands-on familiarity with the subject matter. The exam tests applied knowledge and architectural judgment, not just memorization. If you can reason about trade-offs and real-world scenarios, structured practice will handle the rest.

Domain breakdown

The MLS-C01 exam is built around official domains, each with a fixed percentage of the question pool. This distribution should directly inform how you allocate your study time.

Domain

Weight

Focus areas

Data Engineering

20%

S3 data lakes for ML, Glue ETL for feature preparation, Kinesis for real-time ML data ingestion, data labelling with SageMaker Ground Truth, and feature stores with SageMaker Feature Store.

Exploratory Data Analysis

24%

Statistical analysis concepts (distributions, correlation, outlier detection), feature engineering techniques (normalisation, one-hot encoding, imputation), and AWS tools including SageMaker Data Wrangler and SageMaker Clarify.

Modeling

36%

SageMaker training jobs, built-in algorithms (XGBoost, Linear Learner, BlazingText, DeepAR, Image Classification), model tuning with Hyperparameter Optimisation, SageMaker Debugger, and bias/variance trade-offs.

ML Implementation & Operations

20%

SageMaker endpoint types (real-time, serverless, async, batch transform), model monitoring with SageMaker Model Monitor, A/B testing with production variants, CI/CD for ML with SageMaker Pipelines, and model registry.

Note the domain with the highest weight — many candidates under-invest here because it feels conceptual. In practice, this is where the exam is most precise, with scenario-based questions that test specifics.

What the exam actually tests

This is not a memorization exam. Questions require applied judgment under constraints. Almost every question includes a scenario with explicit requirements and asks you to select the most appropriate solution.

Here are examples of the question types you will encounter:

Algorithm selection

"A retail company wants to forecast daily sales for 50,000 products based on three years of historical sales data. Each product has a time-series of daily observations. Which SageMaker built-in algorithm is MOST appropriate?"

Tests knowledge of SageMaker built-in algorithms. DeepAR is the correct answer for time-series forecasting across multiple related time series. The exam tests XGBoost (tabular classification/regression), BlazingText (text classification/word2vec), DeepAR (time-series), and Image Classification scenarios.

Bias and variance diagnosis

"A SageMaker model achieves 98% accuracy on the training set but only 72% accuracy on the validation set. What does this indicate, and what is the BEST corrective action?"

Tests understanding of overfitting (high variance): the model memorised training data but does not generalise. Solutions include regularisation (L1/L2), dropout, reducing model complexity, or gathering more training data. This type of statistical reasoning question appears frequently in the 36% Modeling domain.

Endpoint type selection

"A medical imaging application needs to run inference on MRI scans that are uploaded asynchronously. Each inference job takes 3–5 minutes, and the application can tolerate a few minutes of latency. The workload is intermittent. Which SageMaker inference option is MOST cost-effective?"

Tests SageMaker endpoint types: real-time (low-latency, persistent), serverless (intermittent, spiky traffic, < 60 seconds), asynchronous (long-running, large payloads, queue-based), and batch transform (offline bulk inference). Asynchronous inference is correct here.

How to prepare — 4-week study plan

This plan assumes one hour per weekday and roughly 30 minutes of lighter review on weekends. It is calibrated for someone with some relevant experience. If you are starting from zero, add an extra week before Week 1 to familiarise yourself with the basics.

Week 1: Data Engineering & EDA for ML

Study S3-based data lake patterns for ML: partitioning raw vs processed data, versioning datasets, and using Athena for exploratory queries
Learn SageMaker Ground Truth: labelling job types, workforce options (public/private/vendor), and semi-automated labelling with active learning
Study feature engineering techniques: normalisation vs standardisation, one-hot encoding, target encoding, missing value imputation strategies, and handling class imbalance (oversampling, SMOTE, class weights)
Learn SageMaker Data Wrangler and SageMaker Feature Store: online vs offline feature store, feature groups, and point-in-time correct lookups

Week 2: SageMaker Training & Built-in Algorithms

Study all major SageMaker built-in algorithms: XGBoost (tabular), Linear Learner (classification/regression), BlazingText (word2vec/text classification), DeepAR (time-series forecasting), Image Classification (ResNet)
Learn SageMaker training jobs: instance types for CPU vs GPU workloads, distributed training (data parallel vs model parallel), Pipe mode vs File mode, and Spot Instance training with checkpointing
Understand hyperparameter optimisation (HPO) in SageMaker: Bayesian vs random search, concurrent training jobs, early stopping strategies, and warm start HPO jobs
Study SageMaker Debugger: built-in rules, custom rules, tensor collection, and stopping training automatically when a rule fires

Week 3: ML Operations & Deployment

Learn all four SageMaker inference modes: real-time endpoints (multi-model endpoints, multi-container endpoints), serverless inference, asynchronous inference, and batch transform
Study SageMaker Model Monitor: data quality, model quality, bias drift, and feature attribution drift monitors — and how CloudWatch alarms trigger retraining
Understand SageMaker Pipelines: pipeline steps (Processing, Training, Tuning, Evaluation, Condition, Register), cross-pipeline dependencies, and integration with the model registry
Study A/B testing with SageMaker production variants: traffic weights, shadow variants, and using CloudWatch metrics to promote a variant

Week 4: ML Theory Reinforcement & Mock Exams

Review key ML theory: bias/variance trade-off, regularisation (L1 Lasso vs L2 Ridge), evaluation metrics (precision, recall, F1, AUC-ROC, RMSE) and when each metric is appropriate
Study SageMaker Clarify for bias detection: pre-training bias metrics (CI, DPL), post-training bias metrics (DPPL), and explainability with SHAP values
Complete two full 65-question mock exams under 180-minute timed conditions and review all incorrect answers
Drill SageMaker endpoint type selection and built-in algorithm matching — the two highest-volume question topics on this exam

Common mistakes candidates make

These patterns appear repeatedly among candidates who resit this exam. Knowing them in advance is worth several percentage points.

Underestimating the statistical and ML theory depth

MLS-C01 is one of the hardest AWS exams because 36% of it tests Modeling concepts including bias/variance trade-offs, regularisation techniques, feature engineering decisions, and the correct interpretation of evaluation metrics like AUC-ROC. Candidates with only cloud/ops backgrounds who have not studied ML fundamentals consistently fail this domain.

Weak on SageMaker endpoint type selection

There are four SageMaker inference modes and each has specific use cases. Real-time is for low-latency synchronous requests. Serverless is for intermittent traffic under 60-second inference time. Asynchronous is for long-running inference jobs with large payloads. Batch transform is for offline bulk scoring. Confusing asynchronous with serverless or batch is a common source of exam errors.

Not knowing when to use SageMaker built-in algorithms

The exam frequently presents a business problem and asks which built-in algorithm is most appropriate. Candidates who rely on general ML knowledge (choosing scikit-learn or TensorFlow for everything) miss questions about DeepAR for time-series, BlazingText for text, and Object2Vec for relationship embedding. Memorise the primary use case for each SageMaker built-in algorithm.

Ignoring SageMaker Model Monitor and Clarify

ML operations topics (model monitoring, bias detection, explainability) represent the MLOps aspect of the exam. Candidates who study training and deployment but skip Model Monitor and Clarify are unprepared for questions about detecting data drift, feature attribution drift, and the specific bias metrics (CI, DPL, DPPL) that Clarify computes.

Is Certsqill right for you?

Honestly: Certsqill is built for candidates who have already done some studying and want to convert knowledge into exam performance. If you have never touched the subject, start with a foundational course first — then come to Certsqill when you are ready to practice.

Where Certsqill is strong: question depth, AI-powered explanations, and domain analytics. Every question is mapped to the exam blueprint. When you get something wrong, the AI tutor explains why the right answer is right and why each wrong answer fails under the specific constraints in the question.

Where Certsqill is not a replacement: video courses and hands-on labs. Use Certsqill to test and sharpen — not as your first exposure to a topic you have never encountered.

Ready to start practicing?

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