BRAIN AIF-C01 EXAM | TRAINING AIF-C01 MATERIAL

Brain AIF-C01 Exam | Training AIF-C01 Material

Brain AIF-C01 Exam | Training AIF-C01 Material

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Tags: Brain AIF-C01 Exam, Training AIF-C01 Material, AIF-C01 Study Dumps, Download AIF-C01 Pdf, New Exam AIF-C01 Braindumps

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The customers don't need to download or install excessive plugins or software to get the full advantage from web-based AWS Certified AI Practitioner (AIF-C01) practice tests. Additionally, all operating systems also support this format. The third format is the desktop AIF-C01 practice exam software. It is ideal for users who prefer offline AWS Certified AI Practitioner (AIF-C01) exam practice. This format is supported by Windows computers and laptops. You can easily install this software in your system to use it anytime to prepare for the examination.

Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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 5
  • 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.

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Training AIF-C01 Material, AIF-C01 Study Dumps

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Amazon AWS Certified AI Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

  • A. Reduce the volume of data that is used in training.
  • B. Add hyperparameters to the model.
  • C. Increase the volume of data that is used in training.
  • D. Increase the model training time.

Answer: C


NEW QUESTION # 11
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

  • A. Total training time
  • B. Temperature value
  • C. Amount of data used to train the LLM
  • D. Number of tokens consumed

Answer: D


NEW QUESTION # 12
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)

  • A. Amazon Comprehend
  • B. Amazon Lex
  • C. Amazon Polly
  • D. Amazon Bedrock
  • E. Amazon Rekognition

Answer: A,D

Explanation:
To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.
* Amazon Bedrock:
* Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.
* The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces like chatbots, not for sentiment analysis.
* C. Amazon Polly: Converts text to speech; it doesn't analyze sentiment.
* E. Amazon Rekognition: Analyzes images and videos, not text.


NEW QUESTION # 13
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?

  • A. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
  • B. Gather more data. Use Amazon Rekognition to add custom labels to the data.
  • C. Encrypt and secure training data by using Amazon Macie.
  • D. Configure the security and compliance by using Amazon Inspector.

Answer: A


NEW QUESTION # 14
What does an F1 score measure in the context of foundation model (FM) performance?

  • A. Model speed in generating responses
  • B. Model precision and recall
  • C. Energy efficiency of the model's computations
  • D. Financial cost of operating the model

Answer: B

Explanation:
The F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true positive predictions among all positive predictions made by the model), while recall measures the model's ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of model performance but are not related to the F1 score.


NEW QUESTION # 15
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