A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.Which factor will drive the inference costs?

Amount of data used to train the LLM

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.Which adjustment to an inference parameter should the company make to meet these requirements?

Decrease the length of output tokens

Increase the maximum generation length

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.Which solution will meet this requirement?

Create an Amazon Bedrock knowledge base

Choose a lower temperature value

Create an Amazon Bedrock knowledge base

Enable model invocation logging

A company wants to develop an educational game where users answer questions such as the following: 'A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?'Which solution meets these requirements with the LEAST operational overhead?

Use code that will calculate probability by using simple rules and computations.

Use supervised learning to create a regression model that will predict probability.

Use reinforcement learning to train a model to return the probability.

Use code that will calculate probability by using simple rules and computations.

Use unsupervised learning to create a model that will estimate probability density.

Which functionality does Amazon SageMaker Clarify provide?

Identifies potential bias during data preparation

Integrates a Retrieval Augmented Generation (RAG) workflow

Monitors the quality of ML models in production

Documents critical details about ML models

Identifies potential bias during data preparation

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?

Pairs of user messages and correct user intents

Pairs of chatbot responses and correct user intents

Pairs of user messages and correct chatbot responses

Pairs of user messages and correct user intents

Pairs of user intents and correct chatbot responses

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.Which business objective should the company use to evaluate the effect of the LLM chatbot?

Corporate social responsibility

A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.Which solution will meet these requirements?

Decrease the number of tokens in the prompt.

Customize the model by using fine-tuning.

Decrease the number of tokens in the prompt.

Increase the number of tokens in the prompt.

An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.What should the firm do when developing and deploying the LLM? (Select TWO.)

Include fairness metrics for model evaluation.

Modify the training data to mitigate bias.

Include fairness metrics for model evaluation.

Adjust the temperature parameter of the model.

Modify the training data to mitigate bias.

Avoid overfitting on the training data.

Apply prompt engineering techniques.

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?

Automation of repetitive tasks and orchestration of complex workflows

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

Automation of repetitive tasks and orchestration of complex workflows

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

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

A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.Which solution will meet these requirements?

Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.Which solution will meet these requirements?

Deploy optimized small language models (SLMs) on edge devices.

Deploy optimized large language models (LLMs) on edge devices.

Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.

Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.Which solution meets these requirements?

Transcribe call recordings by using Amazon Transcribe.

Build a conversational chatbot by using Amazon Lex.

Transcribe call recordings by using Amazon Transcribe.

Extract information from call recordings by using Amazon SageMaker Model Monitor.

Create classification labels by using Amazon Comprehend.

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?

Choose an LLM of a different size.

A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.Which strategy will successfully fine-tune the model?

Provide labeled data with the prompt field and the completion field.

Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.

Purchase Provisioned Throughput for Amazon Bedrock.

Train the model on journals and textbooks.

A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.Which solution meets these requirements?

Experiment and refine the prompt until the FM produces the desired responses.

Set a low limit on the number of tokens the FM can produce.

Use batch inferencing to process detailed responses.

Experiment and refine the prompt until the FM produces the desired responses.

Define a higher number for the temperature parameter.

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.Which prompt engineering strategy meets these requirements?

Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.

Provide the new text passage to be classified without any additional context or examples.

Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

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

PartyRock, an Amazon Bedrock Playground

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?

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

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

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

Change the FM inference parameters.

Clean the research paper data to remove complex scientific terms.

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?

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

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

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

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

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

An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.Which solution meets these requirements with the LEAST implementation effort?

Add a role description to the prompt context that instructs the model of the age range that the response should target.

Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.

Add a role description to the prompt context that instructs the model of the age range that the response should target.

Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.

Summarize the response text depending on the age of the user so that younger users receive shorter responses.

Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

Measure the model's accuracy against a predefined benchmark dataset.

Calculate the total cost of resources used by the model.

Measure the model's accuracy against a predefined benchmark dataset.

Count the number of layers in the neural network.

Assess the color accuracy of images processed by the model.

A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.Which solution meets these requirements?

Generative pre-trained transformers (GPT)

Which metric measures the runtime efficiency of operating AI models?

Customer satisfaction score (CSAT)

Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

Improves model performance over time

Helps decrease the model's complexity

Improves model performance over time

Decreases the training time requirement

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.Which action must the company take to use the custom model through Amazon Bedrock?

Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

Purchase Provisioned Throughput for the custom model.

Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

Register the model with the Amazon SageMaker Model Registry.

Grant access to the custom model in Amazon Bedrock.

A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.Which model evaluation strategy meets these requirements?

Bilingual Evaluation Understudy (BLEU)

Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

How can companies use large language models (LLMs) securely on Amazon Bedrock?

Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

Enable AWS Audit Manager for automatic model evaluation jobs.

Enable Amazon Bedrock automatic model evaluation jobs.

Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.

A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.Which AI learning strategy provides this self-improvement capability?

Reinforcement learning with rewards for positive customer feedback

Supervised learning with a manually curated dataset of good responses and bad responses

Reinforcement learning with rewards for positive customer feedback

Unsupervised learning to find clusters of similar customer inquiries

Supervised learning with a continuously updated FAQ database

Amazon AIF-C01 Practice Test 2 - Free Online Exam Questions 41-80

A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.

Which AWS service meets these requirements?

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Amazon AIF-C01 Practice Test 2

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