AIF-C01 Minimum Pass Score | AIF-C01 Useful Dumps

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Amazon AIF-C01 Exam Overview:

Certification Vendor:	Amazon AWS
Exam Name:	AWS Certified AI Practitioner
Exam Number:	AIF-C01
Passing Score:	700 (scaled score 100–1000)
Real Exam Qty:	65 (50 scored, 15 unscored)
Related Certifications:	AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty
Certificate Validity Period:	3 years
Exam Format:	Ordering, Multiple choice, Multiple response
Exam Price:	100 USD
Available Languages:	Japanese, Simplified Chinese, English, Traditional Chinese, Korean
Exam Duration:	90 minutes
Recommended Training:	AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path
Exam Registration:	AWS Certification Registration Pearson VUE Scheduling
Sample Questions:	Amazon AIF-C01 Sample Questions
Exam Way:	Online proctored or testing center delivery
Pre Condition:	No required prerequisites; recommended basic understanding of cloud computing and general IT concepts
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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.
Topic 5	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.

Amazon AWS Certified AI Practitioner Sample Questions (Q366-Q371):

NEW QUESTION # 366
A company wants to assess internet quality in remote areas of the world. The company needs to collect internet speed data and store the data in Amazon RDS. The company will analyze internet speed variation throughout each day. The company wants to create an AI model to predict potential internet disruptions.
Which type of data should the company collect for this task?

A. Time series data
B. Tabular data
C. Text data
D. Audio data

Answer: A

NEW QUESTION # 367
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters

Answer:

Explanation:

NEW QUESTION # 368
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?

A. F1 score
B. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
C. Root mean squared error (RMSE)
D. Bilingual Evaluation Understudy (BLEU)

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 # 369
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

A. Measurement bias
B. Confirmation bias
C. Sampling bias
D. Observer bias

Answer: C

NEW QUESTION # 370
What Is the benefit of fine-tuning a foundation model (FM)?

A. Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.
B. Fine-tuning keeps the FM's knowledge up to date by pre-training the FM on more recent data.
C. Fine-tuning improves the performance of the FM on a specific task by further training the FM on new labeled data.
D. Fine-tuning reduces the FM's size and complexity and enables slower Inference.

Answer: C

NEW QUESTION # 371
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