最實用的Amazon AIF-C01考古題
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Amazon AIF-C01 考試大綱：
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最新的 AWS Certified AI AIF-C01 免費考試真題 (Q160-Q165):
問題 #160
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?
答案：B
解題說明：
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.
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:
Reference:
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/)
問題 #161
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
答案：B
問題 #162
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
答案：C
問題 #163
How can companies use large language models (LLMs) securely on Amazon Bedrock?
答案：C
解題說明：
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.
Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.
Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.
Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.
Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.
AWS AI Practitioner Reference:
Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.
問題 #164
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
答案：B
解題說明：
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
Option A: Use data from only customers who match the demography of the company's overall customer base.
Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
問題 #165
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