Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 21:

  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?

  A. Use few-shot prompting to define how the FM can answer the questions.

  B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

  C. Change the FM inference parameters.

  D. Clean the research paper data to remove complex scientific terms.

  Correct Answer: B

  Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks. Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms":This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance. Option A:"Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms. Option C:"Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology. Option D:"Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution. AWS AI Practitioner References: Domain Adaptation in Amazon Bedrock:AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

• Question 22:

  An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.

  Which problem is the LLM having?

  A. Data leakage

  B. Hallucination

  C. Overfitting

  D. Underfitting

  Correct Answer: B

  In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs)

  when they create information that sounds correct but is actually incorrect or fabricated. Option B (Correct): "Hallucination":This is the correct answer because the problem described involves generating content that sounds factual but is

  incorrect, which is characteristic of hallucination in generative AI models. Option A:"Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating

  incorrect content.

  Option C:"Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.

  Option D:"Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.

  AWS AI Practitioner

  References:

  Large Language Models on AWS:AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.

• Question 23:

  A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.

  Which solution will meet this requirement?

  A. Use a different FM

  B. Choose a lower temperature value

  C. Create an Amazon Bedrock knowledge base

  D. Enable model invocation logging

  Correct Answer: C

  Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's

  private data sources to enhance its responses.

  Option C (Correct): "Create an Amazon Bedrock knowledge base":This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.

  Option A:"Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.

  Option B:"Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data. Option D:"Enable model invocation logging" is incorrect because logging does not help in supplementing the model

  with additional data.

  AWS AI Practitioner

  References:

  Amazon Bedrock and Knowledge Integration:AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.

• Question 24:

  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?

  A. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.

  B. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.

  C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.

  D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.

  Correct Answer: C

  Guardrails for Amazon Bedrock provide mechanisms to filter and control the content generated by models to comply with privacy and policy requirements. Using guardrails ensures that sensitive or personal information is not included in the

  model's responses. Additionally, integrating Amazon CloudWatch alarms allows for real-time notification when a policy violation occurs.

  Option C (Correct): "Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations":This is the correct answer because it directly addresses both the prevention of policy violations

  and the requirement to receive notifications when such violations occur. Option A:"Use Amazon Macie to scan the model's output for sensitive data" is incorrect because Amazon Macie is designed to monitor data in S3, not to filter real-time

  model outputs.

  Option B:"Configure AWS CloudTrail to monitor the model's responses" is incorrect because CloudTrail tracks API activity and is not suited for content moderation.

  Option D:"Implement Amazon SageMaker Model Monitor to detect data drift" is incorrect because data drift detection does not address content moderation or privacy compliance.

  AWS AI Practitioner

  References:

  Guardrails in Amazon Bedrock:AWS provides guardrails to ensure AI models comply with content policies, and using CloudWatch for alerting integrates monitoring capabilities.

• Question 25:

  A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.

  Which solution scope gives the company the MOST ownership of security responsibilities?

  A. Using a third-party enterprise application that has embedded generative AI features.

  B. Building an application by using an existing third-party generative AI foundation model (FM).

  C. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.

  D. Building and training a generative AI model from scratch by using specific data that a customer owns.

  Correct Answer: D

  Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the

  model, and the infrastructure. Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns":This is the correct answer because it involves complete ownership of the model, data,

  and infrastructure, giving the company the highest level of responsibility for security. Option A:"Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the

  security of the AI features embedded within a third-party application. Option B:"Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-

  party model provider.

  Option C:"Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

  AWS AI Practitioner

  References:

  Generative AI Security Scoping Matrix on AWS:AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

• Question 26:

  A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.

  Which solution meets these requirements?

  A. Generative pre-trained transformers (GPT)

  B. Residual neural network

  C. Support vector machine

  D. WaveNet

  Correct Answer: A

  Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.

• Question 27:

  A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?"

  Which solution meets these requirements with the LEAST operational overhead?

  A. Use supervised learning to create a regression model that will predict probability.

  B. Use reinforcement learning to train a model to return the probability.

  C. Use code that will calculate probability by using simple rules and computations.

  D. Use unsupervised learning to create a model that will estimate probability density.

  Correct Answer: C

  The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead. Option C (Correct): "Use code that will calculate probability by using simple rules and computations":This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules. Option A:"Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem. Option B:"Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation. Option D:"Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task. AWS AI Practitioner References: Choosing the Right Solution for AI Tasks:AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

• Question 28:

  A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.

  Which AI learning strategy provides this self-improvement capability?

  A. Supervised learning with a manually curated dataset of good responses and bad responses

  B. Reinforcement learning with rewards for positive customer feedback

  C. Unsupervised learning to find clusters of similar customer inquiries

  D. Supervised learning with a continuously updated FAQ database

  Correct Answer: B

  Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the

  goal of self- improvement based on past interactions and new information. Option B (Correct): "Reinforcement learning with rewards for positive customer feedback":This is the correct answer as reinforcement learning enables the chatbot to

  learn from feedback and adapt its behavior accordingly, providing self- improvement capabilities.

  Option A:"Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions. Option C:"Unsupervised learning to find clusters of similar customer inquiries" is incorrect

  because unsupervised learning does not provide a mechanism for improving responses based on feedback.

  Option D:"Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self- improvement from feedback.

  AWS AI Practitioner

  References:

  Reinforcement Learning on AWS:AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.

• Question 29:

  A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group. Which type of bias is affecting the model output?

  A. Measurement bias

  B. Sampling bias

  C. Observer bias

  D. Confirmation bias

  Correct Answer: B

  Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

• Question 30:

  Which functionality does Amazon SageMaker Clarify provide?

  A. Integrates a Retrieval Augmented Generation (RAG) workflow

  B. Monitors the quality of ML models in production

  C. Documents critical details about ML models

  D. Identifies potential bias during data preparation

  Correct Answer: D

  Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further

  steps in the ML pipeline. Option C (Correct): "Exploratory data analysis":This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

  Option A:"Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

  Option B:"Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure. Option D:"Hyperparameter tuning" is incorrect because it refers to optimizing model parameters,

  not analyzing the data.

  AWS AI Practitioner

  References:

  Stages of the Machine Learning Pipeline:AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.