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【General】 Latest AIF-C01 Mock Test | Reliable AIF-C01 Exam Labs

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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
  • 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 3
  • 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 4
  • 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 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q87-Q92):NEW QUESTION # 87
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?
  • A. Real-time inference
  • B. Asynchronous inference
  • C. Batch transform
  • D. Serverless inference
Answer: A

NEW QUESTION # 88
A company is developing an AI solution to help make hiring decisions.
Which strategy complies with AWS guidance for responsible AI?
  • A. Use the AI solution to make final hiring decisions without human review.
  • B. Train the AI solution exclusively on data from previous successful hires.
  • C. Keep the AI decision-making process confidential to maintain a competitive advantage.
  • D. Test the AI solution to ensure that it does not discriminate against any protected groups.
Answer: D
Explanation:
The correct answer is C - Test the AI solution to ensure that it does not discriminate against any protected groups. According to AWS Responsible AI principles, fairness and bias mitigation are essential when AI is used for high-impact decisions such as hiring. AWS documentation emphasizes evaluating datasets, model outputs, and demographic performance to ensure that AI systems do not reinforce or reproduce discriminatory patterns. Services such as Amazon SageMaker Clarify support automated bias detection and explainability, helping teams identify and mitigate unwanted correlations in training data or model predictions. Option A violates AWS guidance, as human-in-the-loop review is required for sensitive decisions. Option B risks amplifying historical bias because training on only "successful" hires can create feedback loops. Option D contradicts transparency principles, which AWS states are crucial for accountability in regulated or ethical decision-making domains. Therefore, rigorous fairness testing aligns with AWS's recommended practices for responsible AI in hiring workflows.
Referenced AWS Documentation:
* AWS Responsible AI Whitepaper - Fairness and Bias Mitigation
* Amazon SageMaker Clarify Documentation

NEW QUESTION # 89
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
  • A. The temperature is set too high.
  • B. The selected model does not support fine-tuning.
  • C. The Top P value is too high.
  • D. The input tokens exceed the model's context size.
Answer: D
Explanation:
Comprehensive and Detailed
Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
Temperature (A) and Top P (C) control randomness, not input size.
Fine-tuning (B) is irrelevant to input truncation failures.
Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)

NEW QUESTION # 90
A company is implementing intelligent agents to provide conversational search experiences for its customers.
The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database.
Which AWS service will meet these requirements?
  • A. Amazon EMR
  • B. Amazon Athena
  • C. Amazon Redshift
  • D. Amazon Aurora PostgreSQL
Answer: D
Explanation:
The requirement is to identify an AWS database service that supports the storage and querying of embeddings (from a generative AI model) as vectors. Embeddings are typically high-dimensional numerical representations of data (e.g., text, images) used in AI applications like conversational search. The database must support vector storage and efficient vector similarity searches. Let's evaluate each option:
* A. Amazon Athena: Amazon Athena is a serverless query service for analyzing data in Amazon S3 using SQL. It is designed for ad-hoc querying of structured data but does not natively support vector storage or vector similarity searches, making it unsuitable for this use case.
* B. Amazon Aurora PostgreSQL: Amazon Aurora PostgreSQL is a fully managed relational database compatible with PostgreSQL. With the pgvector extension (available in PostgreSQL and supported by Aurora PostgreSQL), it can store and query vector embeddings efficiently. The pgvector extension enables vector similarity searches (e.g., using cosine similarity or Euclidean distance), which is critical for conversational search applications using embeddings from generative AI models.
* C. Amazon Redshift: Amazon Redshift is a data warehousing service optimized for analytical queries on large datasets. While it supports machine learning features and can store numerical data, it does not have native support for vector embeddings or vector similarity searches as of May 17, 2025, making it less suitable for this use case.
* D. Amazon EMR: Amazon EMR is a managed big data platform for processing large-scale data using frameworks like Apache Hadoop and Spark. It is not a database service and is not designed for storing or querying vector embeddings in the context of a conversational search application.
Exact Extract Reference: According to the AWS documentation, "Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension, which enables efficient storage and similarity searches for vector embeddings. This makes it suitable for AI/ML workloads such as natural language processing and recommendation systems that rely on vector data." (Source: AWS Aurora Documentation - Using pgvector with Aurora PostgreSQL, https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide
/PostgreSQLpgvector.html). Additionally, the pgvector extension supports operations like nearest-neighbor searches, which are essential for querying embeddings in a conversational search system.
Amazon Aurora PostgreSQL with the pgvector extension directly meets the requirement for storing and querying embeddings as vectors, making B the correct answer.
:
AWS Aurora Documentation: Using pgvector with Aurora PostgreSQL (https://docs.aws.amazon.com
/AmazonRDS/latest/AuroraUserGuide/PostgreSQLpgvector.html)
AWS AI Practitioner Study Guide (focus on data engineering for AI, including vector databases) AWS Blog on Vector Search with Aurora (https://aws.amazon.com/blogs/database/using-vector-search-with- amazon-aurora-postgresql/)

NEW QUESTION # 91
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 Reference:
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 # 92
......
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