AIF-C01 Certification Exam Questions with AI Explanations

QUESTION: 97

A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?

Amazon CloudWatch
AWS CloudTrail
AWS Trusted Advisor
AWS Config

Answer(s): A

Explanation:

Amazon CloudWatch is a highly scalable AWS service designed for monitoring and observability. It provides real-time monitoring of system metrics, including performance data for ML systems, such as resource utilization
(CPU, memory, etc.), model inference latency, request counts, and errors. CloudWatch enables users to set up alarms, visualize metrics, and automate actions based on performance thresholds, making it ideal for monitoring the performance of ML systems.

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QUESTION: 98

An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?

Create a custom model training job in PartyRock on Amazon Bedrock.
Use Amazon SageMaker JumpStart to create a training job.
Use a custom script to run an Amazon SageMaker AI model training job.
Create a Jupyter notebook on an Amazon EC2 instance. Use the notebook to train the model.

Answer(s): B

Explanation:

SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.

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QUESTION: 99

Which statement describes a generative AI use case for multimodal models?

Deploy multiple scalable and cost-effective versions of a model.
Process large amounts of data to train multiple models.
Write code in multiple programming languages.
Process different data types, such as images, audio, and video.

Answer(s): D

Explanation:

Multimodal generative AI models are designed to process and reason across multiple data types such as images, audio, video, and text within a single model.

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QUESTION: 100

A company wants to use large language models (LLMs) to produce code from natural language code comments.
Which LLM feature meets these requirements?

Text summarization
Text generation
Text completion
Text classification

Answer(s): B

Explanation:

Text generation is the feature of large language models (LLMs) that enables them to produce text based on a given prompt or input. In this scenario, the input would be natural language code comments, and the LLM
would generate code based on those comments. This process involves understanding the context of the comment and generating corresponding code, which is the core functionality of text generation.

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QUESTION: 101

A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics.
Which ML strategy should the company use to meet these requirements?

Unsupervised learning
Supervised learning
Reinforcement learning
Semi-supervised learning

Answer(s): A

Explanation:

Unsupervised learning is used to automatically group or cluster similar customers and products based on their characteristics without the need for labeled data. This strategy fits scenarios where the goal is to discover patterns or groupings in the data.

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QUESTION: 102

A company wants to improve its chatbot's responses to match the company's desired tone. The company has
100 examples of high-quality conversations between customer service agents and customers. The company wants to use this data to incorporate company tone into the chatbot's responses.
Which solution meets these requirements?

Use Amazon Personalize to generate responses.
Create an Amazon SageMaker HyperPod pre-training job.
Host the model by using Amazon SageMaker. Use TensorRT for large language model (LLM) deployment.
Create an Amazon Bedrock fine-tuning job.

Answer(s): D

Explanation:

Bedrock’s fine-tuning lets you adapt a foundation model on your own conversation examples—in this case, the
100 high-quality transcripts to instill your company’s tone directly into the model’s response generation.

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QUESTION: 103

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?

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

Answer(s): B

Explanation:

Adding a role description to the prompt is the simplest and most effective way to adjust the model's response style based on the user's age range. It requires minimal implementation effort and effectively tailors the output.
The other options involve more complex processes, such as fine-tuning or additional reasoning steps.

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QUESTION: 104

A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?

Model size
Training data quality
General-purpose use and high-powered GPU availability
Model latency and optimized inference speed

Answer(s): D

Explanation:

For real-time service quotes, the critical requirement is low response time. A model with optimized inference speed and low latency ensures fast and efficient user interactions, making it the correct selection criterion.

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Amazon AWS Certified AI Practitioner AIF-C01 Dumps in PDF

Free Amazon AIF-C01 Real Questions (page: 55)

QUESTION: 97

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QUESTION: 98

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QUESTION: 99

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QUESTION: 100

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QUESTION: 101

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QUESTION: 102

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QUESTION: 103

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QUESTION: 104

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