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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-mls-certification-machine-learning-specialty-test/
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课程名称：征服AWS MLS-C01考试：机器学习实践测试 概述：***更新日期为2024年3月29日*** 在您的认证旅程中提供技术支持，请使用问答功能提出任何疑问。您获得30天退款保证。 认证的好处包括： - 行业认可：验证您向雇主、潜在客户和同行所具备的技能。 - 职业提升：增强您的职业资历，有助于职业发展机会。 - 社区和网络：为您打开与AWS云认证专业人士的网络大门。 今天就开始您的实践，迈出通往成功职业生涯的信心一步。 **真实且富有挑战性的实践以实现实际成功** 提升您的技能，通过实践考试测试您的AWS专业知识，并识别需要改进的领域。体验类似于正式AWS考试的场景与挑战性问题。 **实践考试介绍** 1. **考试目的和对齐** - 清晰的目标：明确考试测量什么（知识、技能、判断），与专业实践所需的能力紧密相连。 - 符合标准：与最新考试标准和指南对齐，加强考试的有效性和相关性。 2. **实践考试中的问题** - 相关性：聚焦于专业人员可能在实践中遇到的真实场景和问题。 - 认知水平：包括不同思维水平问题的混合：知识/回忆、理解/应用、分析/评估。 - 清晰度：保证问题简洁明确，不使用行话或过于技术化的语言。 - 可靠性：问题能够持续测量预期的知识或技能，减少不同解释的可能性。 - 无欺骗性：避免“陷阱”问题或误导性表述，专注于测试真实理解。 3. **题型** - 多样性：包括适合测试知识/技能的多种问题格式，如多选题、简答题、案例分析等。 - 平衡：确保不同题型之间的平衡，避免过度依赖单一格式。 **本实践考试的主要特点与好处** - 最新且与考试对齐的问题：持续更新以反映最新考试大纲，问题反映正式考试的难度、格式和内容。 - 定期更新：本练习考试不断更新，以反映考试的最新变化，确保您拥有最新的准备资源。 - 每个答案的详细解释：不仅告诉您对错，还提供清晰的解释以巩固概念，并帮助您找到待改进的领域。 - 场景挑战：测试您在复杂真实场景中应用所学原理的能力，如同您在考试中遇到的场景。 - 进度跟踪：监控您的表现， pinpoint需要进一步学习的具体主题。 **选择实践考试的理由** - 提升信心，减轻焦虑：实践使完美！在考试时信心满满，因为您已经面对过类似的挑战问题。 - 成本效益的补充：结合深入学习，实践模拟器提升成功机会，并节省重新考试的费用。 **AWS认证机器学习 - 专业（MLS-C01）考试详细信息** - **目的**：该专业认证验证您在AWS上设计、构建、训练、调整和部署机器学习（ML）模型以解决特定商业问题的专业知识。 - **格式**：多项选择题（MCQ）和多回应问题（MRQ），180分钟完成，在线监考或测试中心可用，支持英语、日语、韩语和简体中文。 - **费用**：300美元（或当地等值）。 - **先决条件**：尽管没有强制要求，AWS强烈建议拥有一至多年在AWS云中开发、架构或运行ML/深度学习工作负载的实践经验。 - **考试内容（领域）**： - 数据工程（20%） - 探索性数据分析（20%） - 建模（34%） - 机器学习实施与运维（26%） **重要通知** - **评分**：100-1000分的比例评分，最低通过分数为750。 - **重考**：可以重考，但两次考试之间有等待期。请查看官方AWS认证网站以获取当前政策。 **成功提示** - 深入的实践经验：这不是理论考试，在AWS上构建和部署ML模型的实际经验至关重要。 - 关注AWS服务：了解AWS ML服务（如SageMaker、Comprehend、Rekognition等）的优势、劣势和使用案例。 - ML生命周期的熟练掌握：熟悉从数据准备到运营化和监控的完整ML工作流。
*Updated dated 29 March 2024***You are technically supported in your certification journey - please use Q & A for any query.You are covered with 30-Day Money-Back Guarantee.***Benefits of CertificationsIndustry Recognition: Validates your skills to employers, potential clients, and peers.Career Advancement: Enhances your professional credentials and can lead to career development opportunities.Community and Networking: Opens the door to a network of AWS Cloud certified professionals.Start your practice today and take a confident step towards a successful career.Realistic & Challenging Practice for Real-World SuccessSharpen Your SkillsPut your AWS expertise to the test and identify areas for improvement with practice Exam. Experience exam-like scenarios and challenging questions that closely mirror the official AWS exam.About the practice exam-1. Exam Purpose and AlignmentClear Objectives: Define exactly what the exam intends to measure (knowledge, skills, judgment). Closely tied to the competencies required for professional practice.Alignment with Standards: The exam aligns with latest exam standards, guidelines. This reinforces the validity and relevance of the exam.2. Questions in the practice exam-Relevance: Focus on real-world scenarios and problems that professionals are likely to encounter in their practice.Cognitive Level: Include a mix of questions that assess different levels of thinking:Knowledge/RecallUnderstanding/ApplicationAnalysis/EvaluationClarity: Best effort - Questions to be concise, unambiguous, and free from jargon or overly technical language.Reliability: Questions to consistently measure the intended knowledge or skill, reducing the chance of different interpretations.No Trickery: Avoided "trick" questions or phrasing intended to mislead. Instead, focus on testing genuine understanding.3. Item TypesVariety: Incorporated diverse question formats best suited to the knowledge/skill being tested. This could include:Multiple-choice questionsShort answerCase studies with extended responseScenario-based questionsSimulations (where applicable)Balance: Ensured a balanced mix of item types to avoid over-reliance on any single format.Key Features & Benefits of this Practice Exam:Up-to-Date & Exam-Aligned Questions: Continuously updated to reflect the latest exam syllabus, our questions mirror the difficulty, format, and content areas of the actual exam.Regular Updates: This practice exam is constantly updated to reflect the latest exam changes and ensure you have the most up-to-date preparation resources.Detailed Explanations for Every Answer: We don't just tell you if you got it right or wrong - we provide clear explanations to reinforce concepts and help you pinpoint areas for improvement.Scenario-Based Challenges: Test your ability to apply learned principles in complex real-world scenarios, just like the ones you'll encounter on the exam.Progress Tracking: Monitor your performance and pinpoint specific topics that require further study.Why Choose Practice Exam ?Boost Confidence, Reduce Anxiety: Practice makes perfect! Arrive at the exam confident knowing you've faced similarly challenging questions.Cost-Effective Supplement: Practice simulators, when combined with thorough studying, enhance your chances of success and save you from costly exam retakes.Comprehensive breakdown of the AWS Certified Machine Learning - Specialty (MLS-C01) exam details:Purpose:This specialty certification validates your expertise in designing, building, training, tuning, and deploying machine learning (ML) models on AWS for specific business problems.It demonstrates proficiency in selecting appropriate AWS services, handling ML workflows, and implementing ML solutions at scale.Format:Multiple-choice and multiple-response questions180 minutes (3 hours) to completeOnline proctored or at a testing centerAvailable in English, Japanese, Korean, and Simplified ChineseCost:$300 USD (or local equivalent)Visit Exam pricing: [invalid URL removed] for additional cost information, including foreign exchange rates.Prerequisites:While none are mandatory, AWS strongly recommends:One or more years of hands-on experience developing, architecting, or running ML/deep learning workloads in the AWS Cloud.In-depth knowledge of ML concepts and algorithmsProficiency with Python and common ML/deep learning frameworksExam Content (Domains):Data Engineering (20%): Data collection, cleansing, transformation, feature engineering, and storage for ML models.Exploratory Data Analysis (20%): Visualization, statistical analysis, and identifying biases for improving your dataset and ML model building.Modeling (34%): Selecting algorithms, model training, hyperparameter tuning, evaluation metrics, framework selection (e.g., SageMaker, TensorFlow, PyTorch), and understanding model optimization techniques.Machine Learning Implementation and Operations (26%): Building ML pipelines, operationalizing models with integration into applications, model deployment, CI/CD for ML, retraining strategies, and model monitoring.Important NotesScoring: Scaled score of 100-1000. Minimum passing score is 750. You won't see your exact percentage score.Retakes: You can retake the exam, although there are waiting periods between attempts. Check the official AWS certification website for the current policy.Tips for SuccessDeep Hands-on Experience: This is not a theoretical exam. Practical experience in building and deploying ML models on AWS is crucial.Focus on AWS Services: Understand the strengths, weaknesses, and use cases of AWS ML services like SageMaker, Comprehend, Rekognition, etc.ML Lifecycle Fluency: Be comfortable with the full ML workflow, from data preparation to operationalization and monitoring.
