Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 81:

  Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

  A. Embeddings

  B. Tokens

  C. Models

  D. Binaries

  Correct Answer: A

  Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models

  improve their understanding of textual information by representing concepts in a continuous vector space. Option A (Correct): "Embeddings":This is the correct term, as embeddings provide a way for models to learn relationships between

  different objects in their input space, improving their understanding and processing capabilities. Option B:"Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.

  Option C:"Models" are the algorithms that use embeddings and other inputs, not the representations themselves.

  Option D:"Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.

  AWS AI Practitioner References:

  Understanding Embeddings in AI and NLP:AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.

• Question 82:

  A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.

  Which type of model meets this requirement?

  A. Topic modeling

  B. Clustering models

  C. Prescriptive ML models

  D. BERT-based models

  Correct Answer: D

  BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.

• Question 83:

  A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.

  Which additional data does the company need to meet these requirements?

  A. Pairs of chatbot responses and correct user intents

  B. Pairs of user messages and correct chatbot responses

  C. Pairs of user messages and correct user intents

  D. Pairs of user intents and correct chatbot responses

  Correct Answer: C

  Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.

• Question 84:

  A company is building an application that needs to generate synthetic data that is based on existing data.

  Which type of model can the company use to meet this requirement?

  A. Generative adversarial network (GAN)

  B. XGBoost

  C. Residual neural network

  D. WaveNet

  Correct Answer: A

  Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create

  realistic data. Option A (Correct): "Generative adversarial network (GAN)":This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on. Option

  B:"XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data. Option C:"Residual neural network" is primarily used for improving the performance of deep networks, not for generating

  synthetic data. Option D:"WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.

  AWS AI Practitioner References:

  GANs on AWS for Synthetic Data Generation:AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.

• Question 85:

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

  A. Integration with Amazon S3 for object storage

  B. Support for geospatial indexing and queries

  C. Scalable index management and nearest neighbor search capability

  D. Ability to perform real-time analysis on streaming data

  Correct Answer: C

  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 capabilityare 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 applicationslike 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:

  The other options do not directly relate to building vector database applications:

  A. Integration with Amazon S3 for object storageis about storing data objects, not vector-based searching or indexing.

  B. Support for geospatial indexing and queriesis related to location-based data, not vectors used in machine learning.

  D. Ability to perform real-time analysis on streaming datarelates to analyzing incoming data streams, which is different from the vector search capabilities.

• Question 86:

  A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.

  Which AWS service or feature will meet these requirements?

  A. AWS PrivateLink

  B. Amazon Macie

  C. Amazon CloudFront

  D. Internet gateway

  Correct Answer: A

  AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.

  Option A (Correct): "AWS PrivateLink":This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards. Option

  B:"Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic. Option C:"Amazon CloudFront" is incorrect because it is a content delivery network service and does

  not provide private network connectivity. Option D:"Internet gateway" is incorrect as it enables internet access, which violates the VPC's no-internet-traffic policy.

  AWS AI Practitioner References:

  AWS PrivateLink Documentation:AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.

• Question 87:

  A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.

  Which AWS service or feature will meet these requirements?

  A. Amazon Rekognition

  B. Amazon Bedrock playgrounds

  C. Guardrails for Amazon Bedrock

  D. Agents for Amazon Bedrock

  Correct Answer: C

  Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience.

  "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels. Option C (Correct): "Guardrails for Amazon Bedrock":This is the

  correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all

  content generated is suitable and appropriate for the intended audience. Option A:"Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it

  does not handle text or story generation. Option B:"Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure

  content appropriateness for specific audiences, such as children.

  Option D:"Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.

  AWS AI Practitioner References:

  Guardrails in Amazon Bedrock:Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.

  Building Safe AI Applications:AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.