NEWEST LATEST AIF-C01 TEST MATERIALS OFFER YOU THE BEST RELIABLE STUDY GUIDE | AWS CERTIFIED AI PRACTITIONER

Newest Latest AIF-C01 Test Materials Offer You The Best Reliable Study Guide | AWS Certified AI Practitioner

Newest Latest AIF-C01 Test Materials Offer You The Best Reliable Study Guide | AWS Certified AI Practitioner

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Tags: Latest AIF-C01 Test Materials, Reliable AIF-C01 Study Guide, Test AIF-C01 Dumps Free, Valid AIF-C01 Test Objectives, Valid Exam AIF-C01 Practice

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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 5
  • 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?

  • A. Model monitoring for class distribution
  • B. Watermark detection for images
  • C. Data augmentation for imbalanced classes
  • D. Retrieval Augmented Generation (RAG)

Answer: C


NEW QUESTION # 39
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

  • A. Enable model invocation logging
  • B. Use a different FM
  • C. Choose a lower temperature value
  • D. Create an Amazon Bedrock knowledge base

Answer: D


NEW QUESTION # 40
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?

  • A. Amazon Lex
  • B. Amazon Translate
  • C. Amazon Comprehend
  • D. Amazon Transcribe

Answer: C


NEW QUESTION # 41
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?

  • A. Use unsupervised learning to create a model that will estimate probability density.
  • B. Use reinforcement learning to train a model to return the probability.
  • C. Use code that will calculate probability by using simple rules and computations.
  • D. Use supervised learning to create a regression model that will predict probability.

Answer: C


NEW QUESTION # 42
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

  • A. Support for geospatial indexing and queries
  • B. Scalable index management and nearest neighbor search capability
  • C. Ability to perform real-time analysis on streaming data
  • D. Integration with Amazon S3 for object storage

Answer: B

Explanation:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
* Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost- effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
* Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
* AWS AI Practitioner References:
* According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
* The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
* A. Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
* B. Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
* D. Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.


NEW QUESTION # 43
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