Amazon Amazon Certifications AIF-C01 Questions & Answers

• Question 31:

  An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.

  How should the AI practitioner prevent responses based on confidential data?

  A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.

  B. Mask the confidential data in the inference responses by using dynamic data masking.

  C. Encrypt the confidential data in the inference responses by using Amazon SageMaker.

  D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

  Correct Answer: A

  When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model. of Each Option: Option A (Correct):"Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock. Option B:"Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model being trained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training. Option C:"Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process. Option D:"Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed. AWS AI Practitioner References: Data Handling Best Practices in AWS Machine Learning:AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models. Amazon Bedrock and Model Training Security:Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.

• Question 32:

  An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

  Which strategy should the AI practitioner use?

  A. Configure AWS CloudTrail as the logs destination for the model.

  B. Enable invocation logging in Amazon Bedrock.

  C. Configure AWS Audit Manager as the logs destination for the model.

  D. Configure model invocation logging in Amazon EventBridge.

  Correct Answer: B

  Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data. Option B

  (Correct): "Enable invocation logging in Amazon Bedrock":This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.

  Option A:"Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs. Option C:"Configure AWS Audit Manager" is incorrect as Audit Manager is used for

  compliance reporting, not specific invocation logging for AI models. Option D:"Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI

  model inputs and outputs.

  AWS AI Practitioner

  References:

  Amazon Bedrock Logging Capabilities:AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

• Question 33:

  A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.

  Which solution will meet these requirements?

  A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus

  B. Data augmentation by using an Amazon Bedrock knowledge base

  C. Image recognition by using Amazon Rekognition

  D. Data summarization by using Amazon QuickSight

  Correct Answer: A

  Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.

• Question 34:

  An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.

  Which AWS service can the company use to meet this requirement?

  A. AWS Audit Manager

  B. AWS Artifact

  C. AWS Trusted Advisor

  D. AWS Data Exchange

  Correct Answer: D

  AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications,

  when new datasets or compliance reports become available.

  Option D (Correct): "AWS Data Exchange":This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.

  Option A:"AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports. Option B:"AWS Artifact" is incorrect as it provides access to AWS's compliance

  reports, not ISVs'.

  Option C:"AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.

  AWS AI Practitioner

  References:

  AWS Data Exchange Documentation:AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.

• Question 35:

  A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs.

  Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?

  A. AWS Audit Manager

  B. AWS CloudTrail

  C. Amazon Fraud Detector

  D. AWS Trusted Advisor

  Correct Answer: B

  AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.

• Question 36:

  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?

  A. Supervised learning

  B. Unsupervised learning

  C. Reinforcement learning

  D. Reinforcement learning from human feedback (RLHF)

  Correct Answer: B

  Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.

• Question 37:

  A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.

  Which evaluation metric should the company use to measure the model's performance?

  A. R-squared score

  B. Accuracy

  C. Root mean squared error (RMSE)

  D. Learning rate

  Correct Answer: B

  Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from

  images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.

  Option B (Correct): "Accuracy":This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model. Option A:"R-squared

  score" is incorrect as it is used for regression analysis, not classification tasks.

  Option C:"Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy. Option D:"Learning rate" is incorrect as it is a hyperparameter for training, not a

  performance metric.

  AWS AI Practitioner

  References:

  Evaluating Machine Learning Models on AWS:AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

• Question 38:

  A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.

  Which action must the company take to use the custom model through Amazon Bedrock?

  A. Purchase Provisioned Throughput for the custom model.

  B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.

  C. Register the model with the Amazon SageMaker Model Registry.

  D. Grant access to the custom model in Amazon Bedrock.

  Correct Answer: B

  To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or

  other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock. Option B (Correct): "Deploy the custom model in an Amazon

  SageMaker endpoint for real-time inference":This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.

  Option A:"Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.

  Option C:"Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference. Option D:"Grant access

  to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.

  AWS AI Practitioner

  References:

  Amazon SageMaker Endpoints:AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

• Question 39:

  An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.

  What should the firm do when developing and deploying the LLM? (Select TWO.)

  A. Include fairness metrics for model evaluation.

  B. Adjust the temperature parameter of the model.

  C. Modify the training data to mitigate bias.

  D. Avoid overfitting on the training data.

  E. Apply prompt engineering techniques.

  Correct Answer: AC

  To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment. A. Include Fairness Metrics for Model Evaluation:

• Question 40:

  An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.

  Which type of FM should the AI practitioner use to power the search application?

  A. Multi-modal embedding model

  B. Text embedding model

  C. Multi-modal generation model

  D. Image generation model

  Correct Answer: A

  A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.