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AIF-C01 Questions and Answers

Question # 6

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

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Question # 7

An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.

Which AWS service can the company use to meet this requirement?

A.

AWS Audit Manager

B.

AWS Artifact

C.

AWS Trusted Advisor

D.

AWS Data Exchange

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Question # 8

A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.

Which solution meets these requirements MOST cost-effectively?

A.

Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.

B.

Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.

C.

Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.

D.

Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

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Question # 9

A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.

After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.

How can the company improve the performance of the chatbot?

A.

Use few-shot prompting to define how the FM can answer the questions.

B.

Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

C.

Change the FM inference parameters.

D.

Clean the research paper data to remove complex scientific terms.

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Question # 10

An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.

How should the AI practitioner prevent responses based on confidential data?

A.

Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.

B.

Mask the confidential data in the inference responses by using dynamic data masking.

C.

Encrypt the confidential data in the inference responses by using Amazon SageMaker.

D.

Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

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Question # 11

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.

Which ML algorithm meets these requirements?

A.

Decision trees

B.

Linear regression

C.

Logistic regression

D.

Neural networks

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Question # 12

A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.

Which solution will meet these requirements?

A.

Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus

B.

Data augmentation by using an Amazon Bedrock knowledge base

C.

Image recognition by using Amazon Rekognition

D.

Data summarization by using Amazon QuickSight

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Question # 13

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.

Which consideration will inform the company's decision?

A.

Temperature

B.

Context window

C.

Batch size

D.

Model size

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Question # 14

A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.

Which Amazon Bedrock pricing model meets these requirements?

A.

On-Demand

B.

Model customization

C.

Provisioned Throughput

D.

Spot Instance

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Question # 15

A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.

Which AWS solution should the company use to automate the generation of graphs?

A.

Amazon Q in Amazon EC2

B.

Amazon Q Developer

C.

Amazon Q in Amazon QuickSight

D.

Amazon Q in AWS Chatbot

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Question # 16

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

A.

Amazon Personalize

B.

Amazon SageMaker JumpStart

C.

PartyRock, an Amazon Bedrock Playground

D.

Amazon SageMaker endpoints

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Question # 17

A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.

Which Amazon SageMaker inference option will meet these requirements?

A.

Batch transform

B.

Real-time inference

C.

Serverless inference

D.

Asynchronous inference

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Question # 18

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.

Data augmentation for imbalanced classes

B.

Model monitoring for class distribution

C.

Retrieval Augmented Generation (RAG)

D.

Watermark detection for images

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Question # 19

A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.

Which ML strategy meets these requirements?

A.

Increase the number of epochs.

B.

Use transfer learning.

C.

Decrease the number of epochs.

D.

Use unsupervised learning.

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Question # 20

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.

Which solution will align the LLM response quality with the company's expectations?

A.

Adjust the prompt.

B.

Choose an LLM of a different size.

C.

Increase the temperature.

D.

Increase the Top K value.

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Question # 21

A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.

Which solution meets these requirements?

A.

Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.

B.

Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.

C.

Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.

D.

Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.

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Question # 22

A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.

What are the key benefits of using Amazon Bedrock agents that could help this retailer?

A.

Generation of custom foundation models (FMs) to predict customer needs

B.

Automation of repetitive tasks and orchestration of complex workflows

C.

Automatically calling multiple foundation models (FMs) and consolidating the results

D.

Selecting the foundation model (FM) based on predefined criteria and metrics

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Question # 23

A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.

Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

A.

Amazon EC2 C series

B.

Amazon EC2 G series

C.

Amazon EC2 P series

D.

Amazon EC2 Trn series

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Question # 24

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?

A.

Build an automatic named entity recognition system.

B.

Create a recommendation engine.

C.

Develop a summarization chatbot.

D.

Develop a multi-language translation system.

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Question # 25

A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.

Which solution scope gives the company the MOST ownership of security responsibilities?

A.

Using a third-party enterprise application that has embedded generative AI features.

B.

Building an application by using an existing third-party generative AI foundation model (FM).

C.

Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.

D.

Building and training a generative AI model from scratch by using specific data that a customer owns.

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Question # 26

A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.

Which solution will meet this requirement?

A.

Use Amazon Inspector to monitor SageMaker Studio.

B.

Use Amazon Macie to monitor SageMaker Studio.

C.

Configure SageMaker to use a VPC with an S3 endpoint.

D.

Configure SageMaker to use S3 Glacier Deep Archive.

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Question # 27

An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

Which strategy should the AI practitioner use?

A.

Configure AWS CloudTrail as the logs destination for the model.

B.

Enable invocation logging in Amazon Bedrock.

C.

Configure AWS Audit Manager as the logs destination for the model.

D.

Configure model invocation logging in Amazon EventBridge.

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