AIF-C01 practice questions 111–120

1. Question 111 · 417 in bank

   Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

   • AEmbeddings
   • BTokens
   • CModels
   • DBinaries

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   Answer: A

   Explanation

   A. Embeddings Explanation: Embeddings are numerical representations of real-world objects, words, phrases, or concepts in a continuous vector space. They enable AI and Natural Language Processing (NLP) models to understand and process textual information by capturing the semantic relationships and contextual meanings of words and phrases.

   Source: Adapted from community discussion — may2021_r

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2. Question 112 · 417 in bank

   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?

   • AUse few-shot prompting to define how the FM can answer the questions.
   • BUse domain adaptation fine-tuning to adapt the FM to complex scientific terms.
   • CChange the FM inference parameters.
   • DClean the research paper data to remove complex scientific terms.

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   Answer: B

   Explanation

   Domain adaptation fine-tuning allows you to fine-tune the foundation model (FM) on a dataset that includes examples of the specific domain, in this case, scientific papers with complex terms. This way, the model can better understand and handle the specialized terminology, improving its accuracy when answering domain-specific questions.

   Source: Adapted from community discussion — jove

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3. Question 113 · 417 in bank

   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?

   • ADecrease the temperature value.
   • BIncrease the temperature value.
   • CDecrease the length of output tokens.
   • DIncrease the maximum generation length.

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   Answer: A

   Explanation

   Lowering the temperature value in an LLM controls the randomness of the model's output. A lower temperature (close to 0) makes the model's predictions more deterministic and consistent, leading to similar outputs for identical prompts. This is particularly beneficial in tasks like sentiment analysis, where consistency and reliability in responses are crucial.

   Source: Adapted from community discussion — Blair77

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4. Question 114 · 417 in bank

   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?

   • ACreate an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
   • BCreate a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
   • CRedact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
   • DCreate 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.

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   Answer: A

   Explanation

   A: Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data. Explanation: To comply with the company's security policy requiring each team to access only their own customer data, the best approach is to create custom service roles in Amazon Bedrock for each team. These roles should have fine-grained permissions, granting access only to the specific Amazon S3 data (e.g., folders or buckets) associated with each team's customers. This ensures compliance with the principle of least privilege. Wrong: D. 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: Giving Bedrock full S3 access is a major security risk. Even with team-specific IAM roles, the Bedrock role could be exploited to access any data in S3.

   Source: Adapted from community discussion — Moon

   References

   • AWS documentation

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5. Question 115 · 417 in bank

   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?

   • AUse Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.
   • BConfigure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.
   • CUse Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
   • DImplement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.

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   Answer: C

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6. Question 116 · 417 in bank

   A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing. Which AWS service meets this requirement?

   • AAmazon Textract
   • BAmazon Personalize
   • CAmazon Lex
   • DAmazon Transcribe

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   Answer: A

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7. Question 117 · 417 in bank

   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?

   • AFine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
   • BAdd a role description to the prompt context that instructs the model of the age range that the response should target.
   • CUse chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
   • DSummarize the response text depending on the age of the user so that younger users receive shorter responses.

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   Answer: B

   Explanation

   Adding a role description to the prompt is a straightforward approach that requires minimal changes to the existing model infrastructure. This method leverages prompt engineering, which is often easier and faster to implement than fine-tuning or retraining a model.

   Source: Adapted from community discussion — Blair77

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8. Question 118 · 417 in bank

   A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks. Which ML strategy meets these requirements?

   • AIncrease the number of epochs.
   • BUse transfer learning.
   • CDecrease the number of epochs.
   • DUse unsupervised learning.

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   Answer: B

   Explanation

   B: Use transfer learning. Explanation: Transfer learning is a machine learning strategy that leverages pre-trained models and adapts them to new but related tasks. This allows the company to avoid building models from scratch, significantly reducing the time and resources required for training. By fine-tuning the pre-trained model on domain-specific data, the company can achieve high performance for the new task without starting from the beginning.

   Source: Adapted from community discussion — Moon

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9. Question 119 · 417 in bank

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

   • ACalculate the total cost of resources used by the model.
   • BMeasure the model's accuracy against a predefined benchmark dataset.
   • CCount the number of layers in the neural network.
   • DAssess the color accuracy of images processed by the model.

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   Answer: B

   Explanation

   B. Measure the model's accuracy against a predefined benchmark dataset: This is the correct strategy for evaluating the performance of a foundation model (FM) in an image classification task. Accuracy is typically evaluated by comparing the model's predictions to the known labels of a benchmark dataset that is representative of the problem domain. This allows you to quantify how well the model is performing.

   Source: Adapted from community discussion — Jessiii

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10. Question 120 · 417 in bank

    Select 2

    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? (Choose two.)

    • AInclude fairness metrics for model evaluation.
    • BAdjust the temperature parameter of the model.
    • CModify the training data to mitigate bias.
    • DAvoid overfitting on the training data.
    • EApply prompt engineering techniques.

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    Answer: AC

    Explanation

    A. Include fairness metrics for model evaluation: Fairness metrics help ensure that the model is not biased against any particular group. This is especially important in fields like accounting, where any biases in automated decisions could lead to unethical outcomes. Fairness metrics provide insight into how well the model treats all data groups equally. C. Modify the training data to mitigate bias: Adjusting the training data to address any identified biases is crucial for developing responsible AI applications. This can involve balancing the dataset or removing biased samples, ensuring the model generalizes fairly across different data types and groups.

    Source: Adapted from community discussion — jove

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AIF-C01 practice, question 111. 417 questions available.