AWS Certified Machine Learning Specialty Passing Score

What Is the AWS Machine Learning Specialty Passing Score?

The AWS Certified Machine Learning Specialty (MLS-C01) exam is scored on a scale of 100 to 1,000. To pass, you need a scaled score of 750 or higher. This is not a percentage – it's a scaled score that accounts for differences in difficulty across exam forms. AWS uses this method to ensure fairness: a 750 on one exam version represents the same level of ability as a 750 on another, even if the questions differ slightly in difficulty.

How Scaled Scoring Differs from Raw Percentage

Many candidates mistakenly think they need 75% of questions correct. That's not true. Scaled scoring converts your raw score (the number of questions you answer correctly) to a standardized scale. The exact conversion formula is not published, but AWS does share that the passing score is 750 on a 100–1000 scale. This means the passing threshold is not necessarily 75% of questions. In fact, because the exam may include unscored (pretest) questions and varying difficulty, the number of questions you can miss varies from one exam form to another. Want to go further? AWS Advanced Networking Specialty Passing Score is a good next step.

Key Facts About the Exam

• Number of questions: 65 questions (60 scored + 5 unscored pretest questions).
• Time limit: 180 minutes (3 hours).
• Question types: Multiple choice and multiple response.
• Cost: $300 USD.
• Delivery: Pearson VUE, online or at a testing center.

How Many Questions Can You Miss?

Since the scaling formula is confidential, we can’t give an exact number. However, based on historical data and candidate reports, the passing raw score typically falls between 60% and 70%. That means you can usually miss 20 to 26 questions out of 65 and still pass. But don’t aim for the minimum. A safer target is to answer at least 75% of questions correctly (about 49 out of 65) to account for any variations in scaling.

Worked Example

Imagine you take the exam and answer 45 questions correctly out of 65. That’s a raw score of 69.2%. Depending on the difficulty of your specific exam form, your scaled score might be anywhere from 720 to 780. If the form is slightly easier, 45 correct might yield a 750. If it’s harder, you might need 47 or 48 correct. This is why you should never rely on a specific raw number – focus on understanding the material deeply and aiming for a high raw score.

Strategic Implications: Which Domains to Prioritize

The exam is divided into four domains, each with a specific percentage of the exam. To maximize your score, allocate your study time according to the weight of each domain. Here’s the breakdown: Readers also benefit from AWS Machine Learning Specialty Blueprint: Every Domain, Every Weight.

Since Modeling carries the most weight, you should invest the most time there. However, don’t neglect the other domains – every domain contributes to your score, and missing too many questions in a low-weight domain can still hurt you.

Prioritize High-Yield Topics

Within each domain, some topics appear more frequently. For example:

• Data Engineering: Data ingestion (Kinesis, Glue), data transformation (ETL), and feature engineering.
• EDA: Data visualization, missing value handling, and statistical tests.
• Modeling: Model selection, hyperparameter tuning, regularization, and evaluation metrics (precision, recall, AUC).
• ML Operations: SageMaker endpoints, monitoring, and model deployment best practices.

Focus on these high-yield areas to “bank” safe marks. For instance, mastering SageMaker’s built-in algorithms and the AWS ML stack can earn you easy points in both Modeling and Operations. Looking for more detail? See AWS Machine Learning Specialty Difficulty: What Actually Makes People Fail.

What This Means for Your Study Plan

Knowing the passing score and how scaling works tells you that you don’t need a perfect score. You can miss a significant number of questions and still pass. But that doesn’t mean you should study less. Instead, use this knowledge to manage exam stress and time. Here are actionable strategies:

• Aim for 80% in practice tests. If you’re scoring 80% on practice exams, you’re likely in a strong position. That gives you a buffer of about 10 extra correct answers beyond the minimum.
• Don’t get stuck on hard questions. If a question takes more than 2 minutes, flag it and move on. You can come back later. Remember, you only need 750 out of 1000, so you can afford to miss a few tough ones.
• Use the process of elimination. Even if you don’t know the answer, eliminate obviously wrong choices. A 50/50 guess is better than a random one.
• Review the AWS official exam guide. It lists the exact skills measured and their weights. Use it as your checklist.

Common Misconceptions

• “I need 75% to pass.” Wrong – the passing score is 750 on a 100–1000 scale, not a percentage.
• “Unscored questions don’t matter.” They don’t count toward your score, but you won’t know which ones they are, so treat every question as scored.
• “I can miss more questions if the exam is hard.” Actually, the scaling adjusts for difficulty, so a harder form may require fewer raw correct answers, but the scaled score needed is always 750.

Final Thoughts

Understanding the AWS Machine Learning Specialty passing score is more than just knowing a number – it’s about strategizing your preparation. With a passing threshold of 750, you have a comfortable margin for error, but you should still aim high. Prioritize the Modeling domain, master the core AWS ML services, and practice with realistic mock tests to build your confidence.

For extra structure, use the free study plans and readiness checks to organize your study time and spot any gaps before exam day.

Gauge your exam readiness with AWS Certified Machine Learning - Specialty exam simulations and pinpoint the topics that still need work.