Question # 1
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. |
B. Enable invocation logging in Amazon Bedrock.
Question # 2
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements? | A. Configure the security and compliance by using Amazon Inspector. | B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
| C. Encrypt and secure training data by using Amazon Macie.
| D. Gather more data. Use Amazon Rekognition to add custom labels to the data.
|
B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
Question # 3
In the context of machine learning, what is overfitting?
| A. A model performs well on training data but poorly on unseen data. | B. A model performs equally well on both training and testing data. | C. A model underperforms on both training and testing data. | D. A model performs poorly on training data but well on unseen data. |
A. A model performs well on training data but poorly on unseen data.
Question # 4
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3). The FM encounters a failure when attempting to access the S3 bucket data. Which solution will meet these requirements?
| A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
| B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
| C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
| D. Ensure that the S3 data does not contain sensitive information.
|
A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key":This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
Option B:"Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
Option C:"Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
Option D:"Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
Managing Access to Encrypted Data in AWS:AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.
Question # 5
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model. Which technique will solve the problem?
| A. Data augmentation for imbalanced classes
| B. Model monitoring for class distribution
| C. Retrieval Augmented Generation (RAG)
| D. Watermark detection for images
|
A. Data augmentation for imbalanced classes
Explanation:
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
Data Augmentation for Imbalanced Classes:
Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
Why Option A is Correct:
Balances Data Distribution:Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
Improves Model Fairness:Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
Why Other Options are Incorrect:
B. Model monitoring for class distribution:Helps identify bias but does not actively correct it.
C. Retrieval Augmented Generation (RAG):Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
D. Watermark detection for images:Detects watermarks in images, not a technique for addressing bias.
Question # 6
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements? | A. Evaluate the models by using built-in prompt datasets. | B. Evaluate the models by using a human workforce and custom prompt datasets. | C. Use public model leaderboards to identify the model. | D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models. |
B. Evaluate the models by using a human workforce and custom prompt datasets.
Question # 7
A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs? | A. Amazon EC2 C series | B. Amazon EC2 G series
| C. Amazon EC2 P series
| D. Amazon EC2 Trn series
|
D. Amazon EC2 Trn series
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