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?
A. Set a low limit on the number of tokens the FM can produce.
B. Use batch inferencing to process detailed responses.
C. Experiment and refine the prompt until the FM produces the desired responses.
D. Define a higher number for the temperature parameter.
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?
A. Amazon S3
B. Amazon Elastic Block Store (Amazon EBS)
C. Amazon Elastic File System (Amazon EFS)
D. AWS Snowcone
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 sameinput prompt. Which adjustment to an inference parameter should the company make to meet these requirements?
A. Decrease the temperature value
B. Increase the temperature value
C. Decrease the length of output tokens
D. Increase the maximum generation length
Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?
A. Calculate the total cost of resources used by the model.
B. Measure the model's accuracy against a predefined benchmark dataset.
C. Count the number of layers in the neural network.
D. Assess the color accuracy of images processed by the model.
Which option is a use case for generative AI models?
A. Improving network security by using intrusion detection systems
B. Creating photorealistic images from text descriptions for digital marketing
C. Enhancing database performance by using optimized indexing
D. Analyzing financial data to forecast stock market trends
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?
A. Code for model training
B. Partial dependence plots (PDPs)
C. Sample data for training
D. Model convergence tables
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?
A. Bilingual Evaluation Understudy (BLEU)
B. Root mean squared error (RMSE)
C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
D. F1 score
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 alignswith the company's brand voice and messaging requirements. Which solution meets these requirements?
A. Optimize the model's architecture and hyperparameters to improve the model's overallperformance.
B. Increase the model's complexity by adding more layers to the model's architecture.
C. Create effective prompts that provide clear instructions and context to guide the model'sgeneration.
D. Select a large, diverse dataset to pre-train a new generative model.
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists canperform machine learning (ML) tasks. Which service will meet these requirements?
A. Amazon Lex
B. Amazon Rekognition
C. Amazon Kinesis Data Streams
D. AWS Glue
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance. Which metric will help the AI practitioner evaluate the performance of the model?
A. Confusion matrix
B. Correlation matrix
C. R2 score
D. Mean squared error (MSE)