AIF-C01 practice questions 21–30

1. Question 21 · 417 in bank

   Which phase of the ML lifecycle determines compliance and regulatory requirements?

   • AFeature engineering
   • BModel training
   • CData collection
   • DBusiness goal identification

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   Answer: D

   Explanation

   The business goal identification phase is where the organization defines the purpose of the ML project and determines the compliance, regulatory, and legal requirements. These considerations must be addressed early in the lifecycle to ensure the solution adheres to applicable laws and standards. For example, in industries like finance or healthcare, this phase would identify data privacy regulations (e.g., GDPR, HIPAA) or fairness requirements that need to be incorporated into the ML workflow.

   Source: Adapted from community discussion — ap6491

   References

   • AWS documentation

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2. Question 22 · 417 in bank

   A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy. Which solution meets these requirements?

   • AUse Amazon SageMaker and iterate with newer data.
   • BUse Amazon Personalize and iterate with historical data.
   • CUse Amazon CloudWatch to analyze customer orders.
   • DUse Amazon Rekognition to optimize the model.

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   Answer: A

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3. Question 23 · 417 in bank

   A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure. Which solution meets these requirements?

   • ADeploy the model on an Amazon EC2 instance.
   • BDeploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
   • CDeploy the model by using Amazon CloudFront with an Amazon S3 integration.
   • DDeploy the model by using an Amazon SageMaker endpoint.

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   Answer: D

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4. Question 24 · 417 in bank

   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?

   • ACreate software snippets, reference tracking, and open source license tracking.
   • BRun an application without provisioning or managing servers.
   • CEnable voice commands for coding and providing natural language search.
   • DConvert audio files to text documents by using ML models.

   Show answer

   Answer: A

   Explanation

   A: Create software snippets, reference tracking, and open source license tracking. Explanation: Amazon Q Developer is a generative AI tool designed to assist developers by increasing productivity. It helps in generating software snippets, automating reference tracking, and managing open-source licenses, which directly benefits the software development lifecycle.

   Source: Adapted from community discussion — Moon

   References

   • AWS documentation
   • AWS documentation

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5. Question 25 · 417 in bank

   A company wants to develop an AI application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?

   • AUse Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.
   • BUse Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
   • CUse Amazon Personalize with Amazon Bedrock knowledge bases to build the application.
   • DUse Amazon SageMaker to build the application by training a new ML model.

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   Answer: B

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6. Question 26 · 417 in bank

   A manufacturing company uses AI to inspect products and find any damages or defects. Which type of AI application is the company using?

   • ARecommendation system
   • BNatural language processing (NLP)
   • CComputer vision
   • DImage processing

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   Answer: C

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7. Question 27 · 417 in bank

   A company wants to create an ML model to predict customer satisfaction. The company needs fully automated model tuning. Which AWS service meets these requirements?

   • AAmazon Personalize
   • BAmazon SageMaker
   • CAmazon Athena
   • DAmazon Comprehend

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   Answer: B

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8. Question 28 · 417 in bank

   Which technique can a company use to lower bias and toxicity in generative AI applications during the post-processing ML lifecycle?

   • AHuman-in-the-loop
   • BData augmentation
   • CFeature engineering
   • DAdversarial training

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   Answer: A

   Explanation

   The question specifies reducing bias and toxicity during post-processing of generated content. A. Human-in-the-loop: This is the correct answer. Human review of generated outputs allows for filtering or modification of biased or toxic content after generation. B. Data augmentation: This occurs during training, modifying the training data itself, not the generated outputs. C. Feature engineering: Also a training phase activity, focusing on input features, not generated content. D. Adversarial training: Used during training to improve robustness, not to filter post-generation content

   Source: Adapted from community discussion — Moon

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9. Question 29 · 417 in bank

   A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics. How should the bank fix this issue MOST cost-effectively?

   • AInclude more diverse training data. Fine-tune the model again by using the new data.
   • BUse Retrieval Augmented Generation (RAG) with the fine-tuned model.
   • CUse AWS Trusted Advisor checks to eliminate bias.
   • DPre-train a new LLM with more diverse training data.

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   Answer: A

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10. Question 30 · 417 in bank

    HOTSPOT - A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team. Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times.

    

    This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

    Show the answer figure

    Answer: See figure below

    Explanation

    Scenario 1: The company’s chatbot needs predictions from the LLM to understand users’ intent with minimal latency. Answer: Real-time inference Scenario 2: A data processing job needs to query the LLM to process gigabytes of text files on weekends. Answer: Batch transform Scenario 3: The company’s engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions. Answer: Real-time inference

    Source: Adapted from community discussion — LonghornFan

    

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AIF-C01 practice, question 21. 417 questions available.