Base all questions on the https://d1.awsstatic.com/training-and-certification/docs-ai-practitioner/AWS-Certified-AI-Practitioner_Exam-Guide.pdf and derive from the attached content (Mapping of AI, ML, Deep Learning, and Related Concepts.pdf, practice-exam.pdf, terms.png). Create concise case study questions, each presented in a short paragraph outlining a realistic challenge. The questions should ask for the best option(s) based on specific criteria and require critical evaluation. Each case study will be followed by multiple-choice options. Please generate a mix of medium-complexity questions, blending AWS AI/ML services with general AI/ML concepts.
- All questions should be multiple-choice and present 4 options labeled A, B, C, and D.
- Specify when two correct responses are required and only award full credit if both answers are correct.
- Ensure the scenarios are clear and focus on key concepts from the AWS-Certified-AI-Practitioner exam guide and the other attached content.
- For each multiple-choice question, provide feedback based on whether the selected answer(s) are "correct" or "incorrect".
- If the answer is incorrect or incomplete, provide a brief explanation for improvement.
- Only mark the response as "correct" if the selected answer fully satisfies the case study requirements.
- Use a scale from 100 to 1,000 points, with 700 as the passing threshold.
- For questions requiring two correct answers, full points are awarded only if both answers are correct; partial credit is not given.
- Cumulative scoring should be provided after every 10 questions, with a breakdown of correct answers.
- Present one multiple-choice question at a time and wait for my response.
- After every 10 questions, provide a cumulative score and state how many questions have been answered in total.
- Provide feedback after each question, including explanations for incorrect answers.
You're working for a company that is building a machine learning-based product recommendation system. The team needs to choose the most suitable AWS services to deploy and manage the machine learning models. Based on scalability and integration needs, which two AWS services would be the most appropriate choices?
- A) Amazon SageMaker
- B) AWS Lambda
- C) AWS Glue
- D) Amazon Rekognition
Select two. Both answers must be correct to receive full credit. Feedback will be provided if the answer is incorrect or only partially correct.
Please summarize the areas where I need improvement based on my incorrect answers.
Focus future questions on these specific areas: [enter the questions and explanations I got wrong].
Base all questions on the AWS-Certified-AI-Practitioner exam guide and the content below. Create questions in the form of concise case studies, each presented in a short paragraph. The case studies should outline a realistic challenge and ask for the best option based on specific criteria. Ensure the scenarios are clear, with multiple choices that require critical evaluation.
Alternate between open-ended and multiple-choice questions. Utilize evidence-based learning strategies such as retrieval practice, spaced repetition, and principles from Make It Stick: The Science of Successful Learning by Peter C. Brown, et al.
- One Question at a Time: Present one question (either open-ended or multiple-choice) and wait for my response.
-
Open-ended questions: Mark each as either "correct" or "incorrect" based on whether the response adequately covers key content and main points, without requiring perfection.
-
Multiple-choice questions:
- Present answer options in alpha format (A, B, C, D).
- For standard multiple-choice questions, mark responses as "correct" or "incorrect" based on the selected letter.
- For questions requiring two correct responses, clearly specify this when presenting the question. Only mark the response as "correct" if both selections are correct. If one or both answers are incorrect, provide an explanation for improvement.
- If the answer is incorrect or incomplete, provide a brief explanation for improvement and move to the next question.
- Alternate between open-ended and multiple-choice questions in each round.
- Every 10 questions (5 open-ended, 5 multiple-choice), calculate my score.
- Use a scale from 100 (base score) to 1,000 points, with 700 as the passing threshold.
- For multiple-choice questions requiring two correct responses, full points are awarded only if both answers are correct; partial credit is not given.
- Provide cumulative scoring after every 10 questions and state how many total questions have been answered.
- Provide step-by-step instructions and feedback after each response.
- Clearly communicate how each question contributes to the overall score.
You're working for a company that is developing a machine learning-based product recommendation system. The team needs to choose the most suitable AWS services to deploy and manage the machine learning models. Based on scalability and integration needs, which two AWS services would be the most appropriate choices?
- A) Amazon SageMaker
- B) AWS Lambda
- C) AWS Glue
- D) Amazon Rekognition
Select two. Both answers must be correct to receive full credit. Feedback will be provided if the answer is incorrect or only partially correct.
Create a multiple-choice quiz focusing on the topics provided below, using the attached study guide for reference. The quiz should be administered one question at a time. For each question:
- Present the question and answer choices.
- Wait for my response before providing any feedback.
- After receiving my response, provide a detailed explanation, indicating whether my answer was correct or incorrect.
- Then, proceed to the next question.
[List the topics for improvement based on incorrect answers or areas of focus]
Which AWS service is primarily used for building, training, and deploying machine learning models?
- A) AWS Lambda
- B) Amazon SageMaker
- C) Amazon Rekognition
- D) AWS Glue
[Wait for my answer before proceeding]
Correct Answer: B) Amazon SageMaker
Explanation: Amazon SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models quickly and efficiently.