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?

Data augmentation for imbalanced classes

Model monitoring for class distribution

Retrieval Augmented Generation (RAG)

A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.Which solution meets these requirements?

Create effective prompts that provide clear instructions and context to guide the model's generation.

Optimize the model's architecture and hyperparameters to improve the model's overall performance.

Increase the model's complexity by adding more layers to the model's architecture.

Create effective prompts that provide clear instructions and context to guide the model's generation.

Select a large, diverse dataset to pre-train a new generative model.

A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.What should the company do to meet these requirements?

Evaluate the models by using a human workforce and custom prompt datasets.

Evaluate the models by using built-in prompt datasets.

Evaluate the models by using a human workforce and custom prompt datasets.

Use public model leaderboards to identify the model.

Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.

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?

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

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

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

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

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

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?

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

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

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

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

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

Scalable index management and nearest neighbor search capability

Integration with Amazon S3 for object storage

Support for geospatial indexing and queries

Scalable index management and nearest neighbor search capability

Ability to perform real-time analysis on streaming data

A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.Which solution will meet these requirements?

Use Amazon SageMaker Serverless Inference to deploy the model.

Use Amazon CloudFront to deploy the model.

Use Amazon API Gateway to host the model and serve predictions.

Use AWS Batch to host the model and serve predictions.

A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.Which methodology should the company use to meet these requirements?

Reinforcement learning from human feedback (RLHF)

A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.What should the AI practitioner include in the report to meet the transparency and explainability requirements?

Partial dependence plots (PDPs)

Which option is a use case for generative AI models?

Creating photorealistic images from text descriptions for digital marketing

Improving network security by using intrusion detection systems

Creating photorealistic images from text descriptions for digital marketing

Enhancing database performance by using optimized indexing

Analyzing financial data to forecast stock market trends

A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.Which actions should the company take to meet these requirements? (Select TWO.)

Detect imbalances or disparities in the data.

Evaluate the model's behavior so that the company can provide transparency to stakeholders.

Detect imbalances or disparities in the data.

Ensure that the model runs frequently.

Evaluate the model's behavior so that the company can provide transparency to stakeholders.

Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

Ensure that the model's inference time is within the accepted limits.

A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.Which solution will meet these requirements?

Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.

Configure the security and compliance by using Amazon Inspector.

Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.

Encrypt and secure training data by using Amazon Macie.

Gather more data. Use Amazon Rekognition to add custom labels to the data.

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?

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

Data augmentation by using an Amazon Bedrock knowledge base

Image recognition by using Amazon Rekognition

Data summarization by using Amazon QuickSight

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?

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

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

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

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

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

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?

Generative adversarial network (GAN)

A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.What can Amazon Q Developer do to help the company meet these requirements?

Enable voice commands for coding and providing natural language search.

Create software snippets, reference tracking, and open-source license tracking.

Run an application without provisioning or managing servers.

Enable voice commands for coding and providing natural language search.

Convert audio files to text documents by using ML models.

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.Which solution will meet these requirements?

Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.

Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.

Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

What are tokens in the context of generative AI models?

Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.

Tokens are the mathematical representations of words or concepts used in generative AI models.

Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.

Tokens are the specific prompts or instructions given to a generative AI model to generate output.

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?

Enable invocation logging in Amazon Bedrock.

Configure AWS CloudTrail as the logs destination for the model.

Enable invocation logging in Amazon Bedrock.

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

Configure model invocation logging in Amazon EventBridge.

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?

Configure SageMaker to use a VPC with an S3 endpoint.

Use Amazon Inspector to monitor SageMaker Studio.

Use Amazon Macie to monitor SageMaker Studio.

Configure SageMaker to use a VPC with an S3 endpoint.

Configure SageMaker to use S3 Glacier Deep Archive.

A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.Which solution meets these requirements?

Develop an anomaly detection system.

Build a speech recognition system.

Create a natural language processing (NLP) named entity recognition system.

Develop an anomaly detection system.

Create a fraud forecasting system.

A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.Which action will reduce these risks?

Create a prompt template that teaches the LLM to detect attack patterns.

Increase the temperature parameter on invocation requests to the LLM.

Avoid using LLMs that are not listed in Amazon SageMaker.

Decrease the number of input tokens on invocations of the LLM.

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.What should the company do to mitigate this problem?

Increase the volume of data that is used in training.

Reduce the volume of data that is used in training.

Add hyperparameters to the model.

Increase the volume of data that is used in training.

Increase the model training time.

A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.The data is encrypted with Amazon S3 managed keys (SSE-S3).The FM encounters a failure when attempting to access the S3 bucket data.Which solution will meet these requirements?

Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.

Set the access permissions for the S3 buckets to allow public access to enable access over the internet.

Use prompt engineering techniques to tell the model to look for information in Amazon S3.

Ensure that the S3 data does not contain sensitive information.

Amazon AIF-C01 Practice Test 1 - Free Online Exam Prep & Questions

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?

Temperature

Context window

Batch size

Model size

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