Question 51 · 417 in bank
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
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Answer: B
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AIF-C01 practice questions 51–60
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Question 52 · 417 in bank
A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
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Answer: D
Question 53 · 417 in bank
A global financial company has developed an ML application to analyze stock market data and provide stock market trends. The company wants to continuously monitor the application development phases and to ensure that company policies and industry regulations are followed. Which AWS services will help the company assess compliance requirements? (Choose two.)
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Answer: B
Question 54 · 417 in bank
Which metric measures the runtime efficiency of operating AI models?
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Answer: C
Explanation
Average response time refers to the time taken by an AI model to produce a result after receiving an input. It is a critical metric for assessing the runtime efficiency of an AI model during inference, particularly in applications where quick responses are essential, such as in real-time applications or interactive systems.
Source: Adapted from community discussion — jove
Question 55 · 417 in bank
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock. Which solution meets these requirements MOST cost-effectively?
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Answer: D
Question 56 · 417 in bank
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?
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Answer: B
Question 57 · 417 in bank
A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time. Which AWS service or feature meets these requirements?
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Answer: D
Question 58 · 417 in bank
HOTSPOT - A company wants more customized responses to its generative AI models’ prompts. Select the correct customization methodology from the following list for each use case. Each use case should be selected one time.
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.
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Answer: See figure below
Question 59 · 417 in bank
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls. Which solution meets these requirements?
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Answer: B
Explanation
B: Transcribe call recordings by using Amazon Transcribe. Explanation: Amazon Transcribe is designed for converting speech in audio files (such as customer calls) into text. This text can then be analyzed further to extract key information. It is the first step in gaining insights from audio conversations, making it the appropriate solution for the given requirement.
Source: Adapted from community discussion — Moon
Question 60 · 417 in bank
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements?
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Answer: B
Explanation
B: Unsupervised learning Explanation: Unsupervised learning is used when working with unlabeled data, such as the customer data described in this scenario. This methodology allows the company to identify patterns and group similar customers into clusters or tiers without the need for predefined labels. Techniques like clustering (e.g., K-Means or hierarchical clustering) would help classify customers based on shared characteristics for targeted advertisement campaigns. Why not the other options? A: Supervised learning: Supervised learning requires labeled data, which is not available in this case. Labels would need to be provided for each customer, making this approach unsuitable for the given scenario.
Source: Adapted from community discussion — Moon
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