Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 61:

  A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.

  Which capabilities can the company show compliance for? (Select TWO.)

  A. Auto scaling inference endpoints

  B. Threat detection

  C. Data protection

  D. Cost optimization

  E. Loosely coupled microservices

  Correct Answer: BC

  To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security

  threats to the application. Option C (Correct): "Data protection":This is correct because data protection is critical for compliance with privacy and security regulations. Option B (Correct): "Threat detection":This is correct because detecting and

  mitigating threats is essential to maintaining the security posture required for regulatory compliance.

  Option A:"Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.

  Option D:"Cost optimization" is incorrect because it is focused on managing expenses, not compliance.

  Option E:"Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.

  AWS AI Practitioner

  References:

  AWS Compliance Capabilities:AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.

• Question 62:

  A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.

  Which solution meets these requirements MOST cost-effectively?

  A. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.

  B. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.

  C. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine- tuned model to process user prompts.

  D. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.

  Correct Answer: A

  Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost- effectively, the company should avoid unnecessary use of

  resources. Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock":This is the most cost-effective solution. By using prompt engineering, only the

  relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.

  Option B:"Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size

  processed by the model.

  Option C:"Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.

  Option D:"Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.

  AWS AI Practitioner

  References:

  Prompt Engineering for Cost-Effective AI:AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.

• Question 63:

  A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.

  Which solution will meet these requirements?

  A. Decrease the batch size.

  B. Increase the epochs.

  C. Decrease the epochs.

  D. Increase the temperature parameter.

  Correct Answer: B

  Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of

  accuracy. Option B (Correct): "Increase the epochs":This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.

  Option A:"Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.

  Option C:"Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy. Option D:"Increase the temperature parameter" is incorrect because temperature affects the

  randomness of predictions, not model accuracy.

  AWS AI Practitioner

  References:

  Model Training Best Practices on AWS:AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

• Question 64:

  A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.

  What are the key benefits of using Amazon Bedrock agents that could help this retailer?

  A. Generation of custom foundation models (FMs) to predict customer needs

  B. Automation of repetitive tasks and orchestration of complex workflows

  C. Automatically calling multiple foundation models (FMs) and consolidating the results

  D. Selecting the foundation model (FM) based on predefined criteria and metrics

  Correct Answer: B

  Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is

  crucial. Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows":This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes,

  improving response times and efficiency.

  Option A:"Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models. Option C:"Automatically calling multiple foundation models (FMs) and consolidating the

  results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.

  Option D:"Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.

  AWS AI Practitioner

  References:

  Amazon Bedrock Documentation:AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

• Question 65:

  A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.

  Which ML strategy meets these requirements?

  A. Increase the number of epochs.

  B. Use transfer learning.

  C. Decrease the number of epochs.

  D. Use unsupervised learning.

  Correct Answer: B

  Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.

• Question 66:

  A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing.

  Which AWS service meets this requirement?

  A. Amazon Textract

  B. Amazon Personalize

  C. Amazon Lex

  D. Amazon Transcribe

  Correct Answer: A

  Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.

• Question 67:

  A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.

  Which solution meets these requirements?

  A. Build a speech recognition system.

  B. Create a natural language processing (NLP) named entity recognition system.

  C. Develop an anomaly detection system.

  D. Create a fraud forecasting system.

  Correct Answer: C

  An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.

• Question 68:

  A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.

  Which ML algorithm meets these requirements?

  A. Decision trees

  B. Linear regression

  C. Logistic regression

  D. Neural networks

  Correct Answer: A

  Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model

  arrives at a certain decision, making it suitable for classifying genes into categories. Option A (Correct): "Decision trees":This is the correct answer because decision trees provide a clear and interpretable representation of how input features

  influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.

  Option B:"Linear regression" is incorrect because it is used for regression tasks, not classification.

  Option C:"Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes. Option D:"Neural networks" is incorrect because they are often considered "black boxes" and do not

  easily explain how they arrive at their outputs.

  AWS AI Practitioner References:

  Interpretable Machine Learning Models on AWS:AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.

• Question 69:

  A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.

  Which solution will meet these requirements?

  A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

  B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

  C. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

  D. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

  Correct Answer: A

  To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.

• Question 70:

  A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.

  Which solution will meet this requirement?

  A. Use Amazon Inspector to monitor SageMaker Studio.

  B. Use Amazon Macie to monitor SageMaker Studio.

  C. Configure SageMaker to use a VPC with an S3 endpoint.

  D. Configure SageMaker to use S3 Glacier Deep Archive.

  Correct Answer: C

  To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.