Amazon AIF-C01 Question Answers

Question # 1

A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions  about their loans. The bank wants to ensure that the model does not reveal any private customer data. Which solution meets these requirements? 

A. Use Amazon Bedrock Guardrails.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
C. Increase the Top-K parameter of the LLM.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.

Question # 2

Sentiment analysis is a subset of which broader field of AI? 
 

A. Computer vision
B. Robotics
C. Natural language processing (NLP)
D. Time series forecasting

Question # 3

Which prompting technique can protect against prompt injection attacks? 
 

A. Adversarial prompting
B. Zero-shot prompting
C. Least-to-most prompting
D. Chain-of-thought prompting

Question # 4

A company wants to develop ML applications to improve business operations and efficiency. Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be  selected one or more times. (Select FOUR.) • Supervised learning • Unsupervised learning 

Answer:


The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.Exact Extract from AWS AI Documents:From the AWS AI Practitioner Learning Path:"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels."(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)Detailed Explanation:
Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.Hotspot Selection Analysis:The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and "Unsupervised learning." The correct selections are:Binary classification: Supervised learningMulti-class classification: Supervised learningK-means clustering: Unsupervised learningDimensionality reduction: Unsupervised learningEach paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.References:AWS AI Practitioner Learning Path: Module on Machine Learning StrategiesAmazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machinelearning/)

Question # 5

A digital devices company wants to predict customer demand for memory hardware. The company does not  have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.  The company needs to perform analysis on internal data and external data. Which solution will meet these requirements? 

A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

Question # 6

A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents  and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs. 

A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based architectures.
B. Nova Micro supports only text data. Nova Lite is optimized for numerical data.
C. Nova Micro supports only text. Nova Lite supports images, videos, and text.
D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.

Question # 7

A company is building an AI application to summarize books of varying lengths. During testing, the  application fails to summarize some books. Why does the application fail to summarize some books? 

A. The temperature is set too high.
B. The selected model does not support fine-tuning.
C. The Top P value is too high.
D. The input tokens exceed the model's context size.

Question # 8

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? 

A. Use Amazon Rekognition moderation.
B. Use Amazon Comprehend toxicity detection.
C. Use Amazon SageMaker AI built-in algorithms to train the model.
D. Use Amazon Polly to monitor comments.

Question # 9

A social media company wants to use a large language model (LLM) for content moderation. The company  wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals. Which data source should the company use to evaluate the LLM outputs with the LEAST administrative  effort? 

A. User-generated content
B. Moderation logs
C. Content moderation guidelines
D. Benchmark datasets

Question # 10

A company that uses multiple ML models wants to identify changes in original model quality so that the  company can resolve any issues. Which AWS service or feature meets these requirements?  
 

A. Amazon SageMaker JumpStart
B. Amazon SageMaker HyperPod
C. Amazon SageMaker Data Wrangler
D. Amazon SageMaker Model Monitor

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