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Title: High Pass-Rate - How to Prepare for Amazon AIF-C01 Efficiently and Easily [Print This Page]

Author: loureed621    Time: yesterday 10:34
Title: High Pass-Rate - How to Prepare for Amazon AIF-C01 Efficiently and Easily
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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 2
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 3
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 4
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 5
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.

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Amazon AWS Certified AI Practitioner Sample Questions (Q101-Q106):NEW QUESTION # 101
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?
Answer: A
Explanation:
The correct answer is B because Amazon SageMaker JumpStart provides pre-built solutions, including training workflows for popular open-source LLMs such as Falcon, LLaMA, and others. It allows practitioners to quickly launch fine-tuning jobs using predefined templates, minimizing operational setup and code complexity.
From AWS documentation:
"Amazon SageMaker JumpStart enables you to fine-tune and deploy foundation models with minimal setup. It provides easy-to-use interfaces and pre-built configurations for training, which significantly reduces the operational overhead required to train models." Explanation of other options:
A . PartyRock is designed for prototyping generative AI apps but does not support model training or fine-tuning.
C . Writing a custom script for SageMaker training is flexible but involves more operational effort, including handling infrastructure configuration.
D Training on EC2 via a Jupyter notebook is fully manual and operationally intensive, including dependency setup, data handling, and resource scaling.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker JumpStart Developer Guide - Fine-tuning Foundation Models AWS Certified Machine Learning Specialty Guide - Model Customization and JumpStart

NEW QUESTION # 102
A company wants to extract key insights from large policy documents to increase employee efficiency.
Answer: C
Explanation:
Comprehensive and Detailed
Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.
Regression predicts numerical values.
Clustering groups similar items.
Classification assigns data into predefined categories.
Reference:
AWS NLP Use Cases - Summarization

NEW QUESTION # 103
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?
Answer: D
Explanation:
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
* Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
* Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
* Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
* Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner References:
* Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

NEW QUESTION # 104
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
Answer: B
Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 105
Which option is an example of unsupervised learning?
Answer: D
Explanation:
Unsupervised learning involves training a model on unlabeled data, letting it find patterns or groupings on its own, without explicit outputs provided. Clustering is a primary unsupervised learning technique.
Option A is correct: Grouping customers based on purchase history (without predefined categories) is clustering, a classic unsupervised task.
B and C are supervised learning (classification and regression, respectively).
D is reinforcement learning, not unsupervised learning.
"Unsupervised learning involves training on data without labels and is often used for clustering or dimensionality reduction." (Reference: AWS Certified AI Practitioner Official Study Guide, AWS ML Concepts)
"Unsupervised learning involves training on data without labels and is often used for clustering or dimensionality reduction." (Reference: AWS Certified AI Practitioner Official Study Guide, AWS ML Concepts)

NEW QUESTION # 106
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