Can You Retake MLS-C01 After Failing? Retake Rules Explained (2026)

Direct answer

Yes, you can retake the AWS Certified Machine Learning – Specialty (MLS-C01) exam after failing. Amazon Web Services allows multiple retake attempts with a mandatory waiting period between attempts. You’ll need to wait 14 days after your failed attempt before you can reschedule, and you’ll pay the full exam fee again ($300 USD as of 2026). There’s no lifetime limit on retakes, but the waiting period gives you time to address knowledge gaps that caused the failure.

The key is using that waiting period strategically. Most MLS-C01 candidates who fail struggle with the Modeling domain (36% of the exam) or get tripped up by the practical application questions in Machine Learning Implementation and Operations (20%). Simply retaking without targeted preparation usually leads to another failure.

MLS-C01 retake rules: the official policy

Amazon Web Services maintains consistent retake policies across all their certification exams, including MLS-C01. Here’s what you need to know about the official rules:

Check Amazon Web Services’s official exam page for the most current retake policy as rules can change. AWS occasionally updates their policies, and you want the most accurate information when planning your retake.

The current policy allows unlimited retake attempts, but with specific restrictions:

• You must wait 14 days between attempts
• Each retake requires paying the full exam fee
• You cannot schedule your retake until the waiting period expires
• The 14-day period starts immediately after your failed attempt, not when you schedule the retake

AWS uses Pearson VUE as their testing partner, and the waiting period is enforced at the scheduling level. You literally cannot book another MLS-C01 exam slot until 14 days have passed. This isn’t negotiable – even if you call AWS or Pearson VUE directly.

The waiting period applies regardless of your score. Whether you scored 600 or 695 (with 720 being the passing score), you still wait the same 14 days. AWS doesn’t offer “close call” exceptions or expedited retakes for near-misses.

How long do you have to wait before retaking MLS-C01?

The mandatory waiting period is 14 days from your failed attempt date. This means if you failed on a Monday, the earliest you can retake is the Monday two weeks later.

Here’s how the timeline works in practice:

Day 0: You fail your MLS-C01 exam Days 1-13: Waiting period – cannot schedule retake Day 14: Earliest possible retake date Day 14+: You can schedule your retake for any available date

The waiting period is calculated in calendar days, not business days. Weekends and holidays count toward your 14-day wait. If you failed on January 1st, you could retake on January 15th (assuming test centers are open).

One important detail: the 14-day period starts when you complete your failed exam, not when you receive your score report. Your score report typically arrives within a few hours, but the countdown begins as soon as you submit your last question.

You cannot circumvent this waiting period by registering under a different name or AWS account. AWS and Pearson VUE track candidates by multiple identifiers, and attempting to bypass the waiting period will result in exam cancellation and potential certification program suspension.

How much does a MLS-C01 retake cost?

Each MLS-C01 retake costs the full exam price: $300 USD. There are no discounts for retakes, regardless of how many times you’ve attempted the exam or how close you came to passing.

This pricing structure means that three failed attempts would cost you $1,200 total – a significant investment that underscores why targeted preparation is crucial.

The $300 fee covers:

• The 3-hour exam session
• Access to Pearson VUE testing centers or online proctoring
• Official score reporting
• Certificate issuance if you pass

AWS doesn’t offer payment plans for exam fees. You must pay the full amount when scheduling your retake. However, some employers have certification reimbursement programs that cover retake fees, so check your benefits before paying out of pocket.

If you’re budget-conscious, consider that $300 could buy substantial study materials: practice exams, video courses, hands-on labs, or even AWS credits for practical experience. Sometimes investing in better preparation is more cost-effective than repeated retake attempts.

How many times can you retake MLS-C01?

There’s no official limit on MLS-C01 retake attempts. You can theoretically retake the exam as many times as needed, as long as you observe the 14-day waiting period between attempts and pay the $300 fee each time.

However, unlimited retakes don’t mean unlimited time. AWS certifications expire, and while MLS-C01 is valid for three years, you don’t want to spend months or years pursuing a single certification. Each failed attempt represents 14 days of waiting plus additional preparation time.

From a practical standpoint, if you’ve failed three or more times, you need to fundamentally change your preparation approach. The exam content isn’t changing between attempts – your understanding and test-taking strategy need to evolve.

Most successful candidates pass within their first three attempts. If you’re on attempt four or five, consider:

• Taking a different AWS certification first (like Solutions Architect Associate) to build foundational knowledge
• Getting hands-on experience with AWS ML services before retaking
• Investing in structured training rather than self-study
• Focusing on one domain at a time rather than broad review

AWS doesn’t publish retake statistics, but based on candidate experiences shared in forums and study groups, the success rate is highest on the second attempt when candidates use the waiting period for targeted preparation.

What changes between your first and second attempt

The fundamental exam content doesn’t change between your attempts, but your approach should evolve significantly. Here’s what typically changes for successful second-attempt candidates:

Your understanding of question patterns: MLS-C01 questions often present scenarios with multiple viable solutions, then ask for the “best” approach given specific constraints. First-time test-takers often focus too heavily on technical correctness while missing the business or architectural requirements that determine the optimal answer.

Domain-specific focus: Your score report shows performance in each domain:

• Data Engineering (20%)
• Exploratory Data Analysis (24%)
• Modeling (36%)
• Machine Learning Implementation and Operations (20%)

Second attempts should concentrate on your weakest domains. If you struggled with Modeling (the largest domain at 36%), that’s where your retake preparation should focus most heavily.

Hands-on experience: Many first-time failures stem from theoretical knowledge without practical application. Between attempts, successful candidates typically get hands-on experience with AWS services like SageMaker, Comprehend, Rekognition, or Glue.

Time management: The MLS-C01 exam includes complex scenario questions that can consume significant time. Second-attempt candidates usually develop better time allocation strategies, spending appropriate time on high-value questions while moving quickly through straightforward ones.

Question interpretation skills: AWS exam questions often include extraneous information designed to test your ability to identify relevant details. Second attempts typically involve better skills at parsing questions for key requirements and constraints.

The exam algorithm may present different questions on your retake, but they’ll cover the same domains and difficulty level. Don’t expect an “easier” version – prepare for the same level of rigor.

How to use the waiting period strategically

The 14-day waiting period isn’t just a hurdle – it’s an opportunity to address specific weaknesses revealed by your failed attempt. Here’s how to maximize those two weeks for MLS-C01 preparation:

Days 1-2: Analyze your score report Your score report breaks down performance by domain. Calculate which domains cost you the most points. If you scored 600/720 (needing 120 more points), focus on domains where improvement will yield the most points.

Days 3-5: Identify specific knowledge gaps Within your weakest domains, pinpoint specific topics. For example, if you struggled with Modeling, was it:

• Algorithm selection for specific use cases?
• Hyperparameter tuning strategies?
• Model evaluation metrics interpretation?
• Regularization techniques?

Days 6-10: Targeted learning Focus exclusively on your identified gaps. For MLS-C01, this often means:

For Data Engineering gaps: Practice with AWS Glue, Data Pipeline, Kinesis, and data transformation scenarios For Exploratory Data Analysis: Work with SageMaker notebooks, feature engineering techniques, and data visualization For Modeling: Deep dive into algorithm selection, ensemble methods, and model optimization For ML Implementation and Operations: Focus on deployment strategies, monitoring, and security considerations

Days 11-13: Practice and review Take practice exams focusing on your weak domains. Don’t take full practice exams – instead, do targeted practice on specific topics.

Day 14: Light review and scheduling Schedule your retake and do light review. Avoid cramming new concepts the day you can reschedule.

The most successful retake candidates treat the waiting period as focused remediation time, not general study time. If your first attempt showed weakness in Modeling (36% of the exam), don’t spend equal time reviewing Data Engineering. Concentrate your effort where it will impact your score most.

The biggest retake mistake MLS-C01 candidates make

The biggest mistake is treating the retake like a fresh start instead of a targeted remediation opportunity. Many candidates ignore their score report feedback and return to broad, general study patterns that didn’t work the first time.

Here are the specific mistakes that lead to multiple failures:

Mistake 1: Ignoring the score report Your score report shows exactly which domains need attention, yet many candidates continue studying topics they already understand while neglecting weak areas. If you performed well in Data Engineering but poorly in Modeling, spending equal time on both domains is counterproductive.

Mistake 2: Focusing on memorization over understanding MLS-C01 tests application and analysis, not memorization. Candidates who failed often try to memorize more facts rather than developing deeper understanding of concepts and their practical applications.

Mistake 3: Avoiding hands-on practice The exam extensively tests practical application of AWS ML services. Candidates who rely solely on reading and video courses without hands-on experience consistently struggle with scenario-based questions.

Mistake 4: Rushing back to retake Some candidates schedule their retake for day 14 and spend the waiting period cramming. Successful retakes require strategic preparation time. If you need more than 14 days to address your knowledge gaps adequately, wait longer.

Mistake 5: Not adjusting test-taking strategy If you ran out of time or struggled with question interpretation, those are test-taking issues, not just knowledge gaps. Retake preparation should include practice with time management and question analysis techniques.

**Mistake

Mistake 6: Using the same study materials If your original study approach led to failure, repeating it won’t produce different results. Many candidates stick with the same books, videos, or practice tests that didn’t prepare them adequately the first time.

The solution is recognizing that your retake is a targeted remediation project, not a general review session. Use your score report as a roadmap, focus on hands-on practice with AWS services, and adjust your test-taking approach based on what went wrong during your first attempt.

MLS-C01 retake success strategies that actually work

Based on analysis of successful retake candidates, certain preparation strategies consistently lead to passing scores on the second attempt. Here’s what works:

Deep dive into AWS service integration scenarios MLS-C01 heavily emphasizes how different AWS services work together in real ML workflows. Instead of studying services in isolation, focus on end-to-end scenarios. For example, understand how data flows from S3 through Glue for preprocessing, into SageMaker for training, then to endpoints for inference, with CloudWatch for monitoring.

Successful retake candidates spend significant time on these integration patterns:

• Data ingestion: Kinesis → S3 → Glue → SageMaker
• Batch processing: S3 → EMR with Spark → SageMaker
• Real-time inference: API Gateway → Lambda → SageMaker endpoint
• Model monitoring: SageMaker → CloudWatch → SNS alerts

Practice scenario-based questions extensively MLS-C01 questions rarely ask “What is Random Forest?” Instead, they present complex business scenarios and ask you to select the optimal ML approach given specific constraints like cost, latency, accuracy requirements, or data characteristics.

Practice realistic MLS-C01 scenario questions on Certsqill — with AI Tutor explanations that show exactly why each answer is right or wrong.

Master the decision frameworks Successful candidates develop systematic approaches to question analysis:

For algorithm selection questions:

1. Identify the ML problem type (classification, regression, clustering, etc.)
2. Analyze data characteristics (size, features, labels available)
3. Consider business constraints (interpretability, latency, cost)
4. Match constraints to algorithm strengths

For architecture questions:

1. Identify data volume and velocity requirements
2. Determine processing patterns (batch vs. streaming)
3. Consider scalability and cost requirements
4. Map requirements to AWS service capabilities

Focus on domain-specific weak areas Rather than broad review, concentrate preparation on specific topics within your weakest domains:

If weak in Data Engineering (20%):

• Data transformation patterns in AWS Glue
• Stream processing with Kinesis and Lambda
• Data lake architectures with S3 and Athena
• ETL pipeline design and optimization

If weak in Exploratory Data Analysis (24%):

• Feature engineering techniques and tools
• Data visualization with SageMaker notebooks
• Statistical analysis and hypothesis testing
• Handling missing data and outliers

If weak in Modeling (36% - largest domain):

• Algorithm selection for specific use cases
• Hyperparameter tuning strategies (random search, Bayesian optimization)
• Model evaluation metrics and interpretation
• Ensemble methods and model combining techniques
• Regularization and overfitting prevention

If weak in ML Implementation and Operations (20%):

• SageMaker deployment options and trade-offs
• Model monitoring and drift detection
• Security considerations and IAM policies
• Cost optimization strategies
• A/B testing frameworks

Common myths about MLS-C01 retakes debunked

Several misconceptions about MLS-C01 retakes can derail your preparation strategy. Here are the most persistent myths and the reality:

Myth 1: “The retake exam is easier” Reality: AWS maintains consistent difficulty across all exam administrations. The retake uses the same question pool and difficulty calibration as your first attempt. What changes is your preparation and understanding, not the exam itself.

Myth 2: “You get different questions on the retake” Reality: You’ll likely see some different questions due to AWS’s large question pool, but the topics, difficulty level, and distribution across domains remain consistent. Don’t expect to avoid topics you struggled with previously.

Myth 3: “Near-miss scores (like 695/720) guarantee success on retake” Reality: A near-miss indicates you’re close, but without targeted preparation addressing specific knowledge gaps, you might score similarly or even lower on the retake. The score breakdown by domain is more important than your overall score.

Myth 4: “You need extensive AWS production experience to pass” Reality: While hands-on experience helps, you can pass with focused lab practice and scenario-based study. Many successful candidates use AWS free tier resources and SageMaker Studio notebooks to gain practical experience without production access.

Myth 5: “Memorizing AWS service features is sufficient” Reality: MLS-C01 tests application and analysis, not memorization. You need to understand when and why to use specific services in different scenarios, not just what features they offer.

Myth 6: “Practice exams predict retake success” Reality: Practice exam scores can indicate readiness, but they don’t perfectly predict success. More importantly, practice exams should be used diagnostically to identify knowledge gaps, not as confidence builders.

The key insight is that retake success depends on addressing the specific issues that caused your initial failure, not on exam difficulty changes or luck.

FAQ: MLS-C01 retake questions

Q: Can I use a different AWS account to bypass the 14-day waiting period?

A: No, AWS and Pearson VUE track candidates across multiple identifiers including name, address, and identification documents. Attempting to bypass the waiting period will result in exam cancellation and potential suspension from the certification program. The 14-day period is strictly enforced and non-negotiable.

Q: If I failed MLS-C01 with a score of 700, do I still have to wait 14 days?

A: Yes, the waiting period applies to all failed attempts regardless of score. Whether you scored 500 or 715 (with 720 being the passing score), you must wait the full 14 days. AWS doesn’t offer exceptions for near-miss scores or expedited retakes for any reason.

Q: Should I focus my retake preparation on domains where I scored lowest, or study everything again?

A: Focus primarily on your lowest-scoring domains, as these offer the highest potential score improvement. If your score report shows “Needs Improvement” in Modeling (36% of exam) but “Competent” in Data Engineering (20% of exam), spend most of your retake preparation time on Modeling topics. Use the domain percentages to calculate where additional points will have the most impact.

Q: How do I know if I should wait longer than the minimum 14 days before retaking?

A: Consider waiting longer if your score report shows “Needs Improvement” in multiple domains or if you scored below 650. The 14-day minimum is just that – a minimum. If you need 4-6 weeks to adequately address knowledge gaps through hands-on practice or structured learning, wait longer. Rushing into a retake with inadequate preparation often leads to another failure and additional cost.

Q: Will my SageMaker and other AWS service experience from the waiting period help with MLS-C01 scenario questions?

A: Yes, hands-on experience with AWS ML services significantly improves performance on scenario-based questions, which make up a large portion of MLS-C01. Focus on practical experience with SageMaker (training, tuning, deployment), data preprocessing with Glue, and integration patterns between services. Even free-tier experimentation can provide valuable context for understanding when and why to use specific approaches in exam scenarios.

Related Articles

• I Failed AWS Certified Machine Learning - Specialty (MLS-C01): What Should I Do Next?
• MLS-C01 Score Report Explained: What Your Result Really Means
• How to Study After Failing MLS-C01: Your Recovery Plan for the Retake
• Why Do People Fail MLS-C01? 8 Common Mistakes to Avoid
• Does Failing MLS-C01 Hurt Your Career? The Honest Answer