AIF-C01 — questions

A company needs to select a generative AI model to build an application. The application must provide responses to users in real time. Which model characteristic should the company consider to meet these requirements?

Which term refers to the instructions given to foundation models (FMs) so that the FMs provide a more accurate response to a question?

A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

A company is developing an ML model to predict customer churn. Which evaluation metric will assess the model's performance on a binary classification task such as predicting churn?

An AI practitioner is evaluating the performance of an Amazon SageMaker model. The AI practitioner must choose a performance metric. The metric must show the ratio of the number of correctly classified items to the total number of correctly and incorrectly classified items. Which metric meets these requirements?

An ecommerce company receives multiple gigabytes of customer data daily. The company uses the data to train an ML model to forecast future product demand. The company needs a solution to perform inferences once each day. Which inference type meets these requirements?

A company has developed a generative AI model for customer segmentation. The model has been deployed in the company's production environment for a long time. The company recently noticed some inconsistency in the model's responses. The company wants to evaluate model bias and drift. Which AWS service or feature meets these requirements?

A company has signed up for Amazon Bedrock access to build applications. The company wants to restrict employee access to specific models available on Amazon Bedrock. Which solution meets these requirements?

Which ML technique uses training data that is labeled with the correct output values?

Which large language model (LLM) parameter controls the number of possible next words or tokens considered at each step of the text generation process?

A company is making a chatbot. The chatbot uses Amazon Lex and Amazon OpenSearch Service. The chatbot uses the company's private data to answer questions. The company needs to convert the data into a vector representation before storing the data in a database. Which type of foundation model (FM) meets these requirements?

A company wants to use a large language model (LLM) to generate product descriptions. The company wants to give the model example descriptions that follow a format. Which prompt engineering technique will generate descriptions that match the format?

A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data. Which solution meets these requirements?

A grocery store wants to create a chatbot to help customers find products in the store. The chatbot must check the inventory in real time and provide the product location in the store. Which prompt engineering technique should the store use to build the chatbot?

A company uses a third-party model on Amazon Bedrock to analyze confidential documents. The company is concerned about data privacy. Which statement describes how Amazon Bedrock protects data privacy?

An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?

An ecommerce company wants to group customers based on their purchase history and preferences to personalize the user experience of the company's application. Which ML technique should the company use?

A company wants to control employee access to publicly available foundation models (FMs). Which solution meets these requirements?

A company has set up a translation tool to help its customer service team handle issues from customers around the world. The company wants to evaluate the performance of the translation tool. The company sets up a parallel data process that compares the responses from the tool to responses from actual humans. Both sets of responses are generated on the same set of documents. Which strategy should the company use to evaluate the translation tool?

An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM). How should the AI practitioner adjust the inference parameter?