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The exam validates a candidate’s ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud.  \nThe exam also validates a candidate’s ability to complete the following tasks:  \n• Select and justify the appropriate ML approach for a given business problem.  \n• Identify appropriate AWS services to implement ML solutions.  \n• Design and implement scalable, cost-optimized, reliable, and secure ML solutions.  \nTarget candidate description  \nThe target candidate should have 2 or more years of experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud.  \nRecommended AWS knowledge  \nThe target candidate should have the following AWS knowledge:  \n• The ability to express the intuition behind basic ML algorithms  \n• Experience performing basic hyperparameter optimization  \n• Experience with ML and deep learning frameworks  \n• The ability to follow model-training best practices  \n• The ability to follow deployment best practices  \n• The ability to follow operational best practices  \nKnowledge that is out of scope for the target candidate  \nThe following list contains knowledge that the target candidate is not expected to have. This list is non-exhaustive. Knowledge in the following areas is out of scope for the exam:  \n• Extensive or complex algorithm development  \n• Extensive hyperparameter optimization  \n• Complex mathematical proofs and computations  \n• Advanced networking and network design  \n• Advanced database, security, and DevOps concepts  \n• DevOps-related tasks for Amazon EMR  \nRefer to the Appendix for a list of technologies and concepts that might appear on the exam, a list of in-scope AWS services and features, and a list of out-of-scope AWS services and features.  \nExam content  \nResponse types  \nThere are two types of questions on the exam:  \n• Multiple choice: Has one correct response and three incorrect responses (distractors)  \n• Multiple response: Has two or more correct responses out of five or more response options  \nSelect one or more responses that best complete the statement or answer the question. Distractors, or incorrect answers, are response options that a candidate withincomplete knowledge or skill might choose. Distractors are generally plausible responses that match the content area.  \nUnanswered questions are scored as incorrect; there is no penalty for guessing. The exam includes 50 questions that affect your score.  \nUnscored content  \nThe exam includes 15 unscored questions that do not affect your score. AWS collects information about performance on these unscored questions to evaluate these questions for future use as scored questions. These unscored questions are not identified on the exam.  \nExam results  \nThe AWS Certified Machine Learning-Specialty (MLS-C01) exam has a pass or fail designation. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.  \nYour results for the exam are reported as a scaled score of 100–1,000. The minimum passing score is 750. Your score shows how you performed on the exam as a whole and whether you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.  \nYour score report could contain a table of classifications of your performance at each section level. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.  \nEach section of the exam has a specific weighting, so some sections have more questions than other sections have. The table o","cbCairIhWl912NAl","https://ap.wps.com/l/cbCairIhWl912NAl","pdf",143783,11,"English","# Introduction\n# Target candidate description\n# Recommended AWS knowledge\n# Knowledge out of scope\n# Exam content\n## Question types\n## Exam results\n## Content outline\n# Domain 1: Data Engineering\n## Task Statement 1.1\n## Task Statement 1.2\n## Task Statement 1.3\n# Domain 2: Exploratory Data Analysis","[{\"question\":\"What does the MLS-C01 exam validate about a candidate’s abilities?\",\"answer\":\"It validates abilities to design, build, deploy, optimize, train, tune, and maintain ML solutions for business problems using AWS Cloud, including selecting an ML approach and relevant AWS services.\"},{\"question\":\"What experience and AWS knowledge are expected for the target candidate?\",\"answer\":\"The guide expects 2+ years developing, architecting, and operating ML/deep learning workloads on AWS, plus familiarity with ML algorithm intuition, basic hyperparameter optimization, frameworks, and model, deployment, and operational best practices.\"},{\"question\":\"How are exam questions scored and how is passing determined?\",\"answer\":\"The exam uses 50 scored questions plus 15 unscored questions. Results are pass/fail with a minimum passing score of 750 on a scaled score from 100 to 1,000, and overall passing is required rather than passing each section individually.\"}]","AWS Certified Machine Learning-Specialty (MLS-C01) Exam Guide | PDF",1789932813,28]