Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 131:

  An ecommerce company is developing an AI application that categorizes product images and extracts specifications. The application will use a high-quality labeled dataset to customize a foundation model (FM) to generate accurate responses.

  Which ML technique will meet these requirements by using Amazon Bedrock?

  A. Apply continued pre-training
  B. Create an agent
  C. Perform fine-tuning
  D. Develop prompt engineering
  C. Perform fine-tuning

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  Explanation

  Fine-tuning customizes a foundation model using a high-quality labeled dataset so the model learns domain-specific patterns, enabling accurate image categorization and specification extraction for the ecommerce use case.

• Question 132:

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

  A. Supervised learning
  B. Unsupervised learning
  C. Reinforcement learning
  D. Transfer learning
  A. Supervised learning

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  Supervised learning trains models on datasets where each example includes both input features and the correct output label, allowing the algorithm to learn the mapping from inputs to known targets.

• Question 133:

  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
  B. Context window

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  The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

• Question 134:

  A chatbot returns overly generic responses. The company wants the model to produce industry-specific answers without retraining the model. Which technique will improve response relevance?

  A. Zero-shot prompting
  B. Temperature = 0
  C. Few-shot prompting
  D. Increase max tokens
  C. Few-shot prompting

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  Few-shot prompting provides examples that teach the model how to respond in a specific domain or style. This improves accuracy and relevance without needing fine-tuning.

• Question 135:

  A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM). Which solution meets these requirements?

  A. Fine-tune an LLM on the company policy text by using Amazon SageMaker.
  B. Select a foundation model (FM) from Amazon Bedrock to build an application.
  C. Create a Retrieval Augmented Generation (RAG) workflow by using Amazon Bedrock Knowledge Bases.
  D. Use Amazon Q Business to build a custom Q App.
  C. Create a Retrieval Augmented Generation (RAG) workflow by using Amazon Bedrock Knowledge Bases.

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  A RAG workflow with Amazon Bedrock Knowledge Bases allows the chatbot to access the most recent company policy documents dynamically, ensuring responses reflect policy updates in near real time without the need to retrain or fine-tune the LLM each time content changes.

• Question 136:

  HOTSPOT

  A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.

  Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all.

  

  

• Question 137:

  A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.

  The company collected a labeled dataset and wants to scale the solution to all product categories.

  Which solution meets these requirements?

  A. Use prompt engineering with zero-shot learning.
  B. Use prompt engineering with prompt templates.
  C. Customize the model with continued pre-training.
  D. Customize the model with fine-tuning.
  D. Customize the model with fine-tuning.

• Question 138:

  A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times. Which solution gives the LLM the ability to use content from previous customer messages?

  A. Turn on model invocation logging to collect messages.
  B. Add messages to the model prompt.
  C. Use Amazon Personalize to save conversation history.
  D. Use Provisioned Throughput for the LLM.
  B. Add messages to the model prompt.

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  To give a large language model (LLM) the ability to use content from previous customer messages in a conversation, the previous messages must be included in the model prompt. This technique is known as prompt engineering and allows the LLM to retain context by incorporating a history of the interaction within the prompt. By appending prior exchanges to the prompt, the model can generate contextually relevant and coherent responses throughout the multi-turn conversation.

• Question 139:

  A company wants to fine-tune a foundation model (FM) by using AWS services. The company needs to ensure that its data stays private, safe, and secure in the source AWS Region where the data is stored. Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  A. Host the model on premises by using AWS Outposts.
  B. Use the Amazon Bedrock API.
  C. Use AWS PrivateLink and a VPC.
  D. Host the Amazon Bedrock API on premises.
  E. Use Amazon CloudWatch logs and metrics.
  B. Use the Amazon Bedrock API.
  C. Use AWS PrivateLink and a VPC.

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  Use the Amazon Bedrock API: You'll call Bedrock's managed fine-tuning endpoints directly in your AWS Region, so your data never leaves that region.

  Use AWS PrivateLink and a VPC: Front Bedrock API traffic through a VPC endpoint via PrivateLink to keep all network traffic on the AWS backbone and within your account's private network.

• Question 140:

  An AI practitioner wants to predict the classification of owers based on petal length, petal width, sepal length, and sepal width. Which algorithm meets these requirements?

  A. K-nearest neighbors (k-NN)
  B. K-mean
  C. Autoregressive Integrated Moving Average (ARIMA)
  D. Linear regression
  A. K-nearest neighbors (k-NN)

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  K-nearest neighbors (k-NN) is a supervised learning algorithm used for classification tasks. It works by classifying a data point based on how its features (such as petal length, petal width, sepal length, and sepal width in this case) are similar to the data points in the training set. For this problem, where the task is to classify flowers based on their features, k-NN is an appropriate choice. k-NN is simple and effective for classification problems where the decision boundary is not necessarily linear, as it looks at the closest neighbors to make predictions.