Helping Hand Questions for AIF-C01

Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.

Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.

Option B: "Model customization" is a feature, not a pricing model.

Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

AWS AI Practitioner References:

AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.

Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM’s performance against known standards with minimal administrative effort.

Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.

Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.

Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.

Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.

AWS AI Practitioner References:

Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

AWS CloudTrail:

Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.

Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.

Why Option B is Correct:

Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access attempts.

Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.

Why Other Options are Incorrect:

A. AWS Audit Manager: Used for automating audit preparation, not for tracking real-time unauthorized access attempts.

C. Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access to AWS services.

D. AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access monitoring.

Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.

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 thepgvectorextension (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, ). Additionally, the pgvector extension supports operations like nearest-neighbor searches, which are essential for querying embeddings in a conversational search system.

Amazon Aurora PostgreSQLwith the pgvector extension directly meets the requirement for storing and querying embeddings as vectors, makingBthe correct answer.