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AIF-C01 Practice Questions

Question # 1
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 # 2
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.
D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.


D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.



Question # 3
Which of the following AWS services is best suited for building and deploying machine learning models without managing infrastructure?
A. Amazon EC2
B. Amazon S3
C. Amazon SageMaker
D. AWS Lambda


C. Amazon SageMaker



Question # 4
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?
A. Training
B. Inference
C. Model deployment
D. Bias correction


B. Inference



Question # 5
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 # 6
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.

Which metric will help the AI practitioner evaluate the performance of the model?
A. Confusion matrix
B. Correlation matrix
C. R2 score
D. Mean squared error (MSE)


A. Confusion matrix



Question # 7
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.


B. Increase the epochs.

Explanation:

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 # 8
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.



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Amazon Web Services AIF-C01 Exam Dumps

Exam Name: AWS Certified AI Practitioner Exam(AI1-C01)
Certification Name: AWS Certified AI Practitioner

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