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*Updated dated 29 March 2024

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Benefits of Certifications

• Industry 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 Success

• Sharpen Your Skills

  • Put 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 Alignment

• Clear 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/Recall

  • Understanding/Application

  • Analysis/Evaluation

• Clarity: 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 Types

• Variety: Incorporated diverse question formats best suited to the knowledge/skill being tested. This could include:

  • Multiple-choice questions

  • Short answer

  • Case studies with extended response

  • Scenario-based questions

  • Simulations (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 questions

• 180 minutes (3 hours) to complete

• Online proctored or at a testing center

• Available in English, Japanese, Korean, and Simplified Chinese

Cost:

• $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 algorithms

  • Proficiency with Python and common ML/deep learning frameworks

Exam 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 Notes

• Scoring: 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 Success

• Deep 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.

Who this course is for:

• Experienced ML Practitioners:
• – Data Scientists: Professionals who already design, build, and train ML models and want to showcase their ability to leverage AWS services for efficient and scalable ML solutions.
• – ML Engineers: Those focused on operationalizing ML models, building automated pipelines, and handling the deployment of ML solutions on AWS.
• Developers Expanding into ML:
• – Software Developers: Developers experienced with AWS who want to broaden their skillset by incorporating ML capabilities into their applications. Having a grasp of ML fundamentals and some Python knowledge is helpful.
• Solutions Architects (With ML Focus):
• – Architects designing ML-powered systems: The certification helps architects make informed choices about AWS ML tools and integration strategies for the solutions they design.