Question # 1 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.
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).
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A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.Question # 2 A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment
analysis. The company wants to classify the sentiment of text passages as positive or
negative. A. Provide examples of text passages with corresponding positive or negative labels in the
prompt followed by the new text passage to be classified.B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.C. Provide the new text passage to be classified without any additional context or
examples.D. Provide the new text passage with a few examples of unrelated tasks, such as text
summarization or question answering.
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A. Provide examples of text passages with corresponding positive or negative labels in the
prompt followed by the new text passage to be classified.
Answer Description Explanation:
Providing examples of text passages with corresponding positive or negative labels in the
prompt followed by the new text passage to be classified is the correct prompt engineering
strategy for using a large language model (LLM) on Amazon Bedrock for sentiment
analysis.
Question # 3 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. A. AWS Audit ManagerB. AWS CloudTrailC. Amazon Fraud DetectorD. AWS Trusted Advisor
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B. AWS CloudTrailQuestion # 4 A company makes forecasts each quarter to decide how to optimize operations to meet
expected demand. The company uses ML models to make these forecasts. A. Code for model trainingB. Partial dependence plots (PDPs)C. Sample data for trainingD. Model convergence tables
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B. Partial dependence plots (PDPs)Question # 5 What does an F1 score measure in the context of foundation model (FM) performance? A. Model precision and recallB. Model speed in generating responsesC. Financial cost of operating the modelD. Energy efficiency of the model's computations
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A. Model precision and recall
Answer Description Explanation: The F1 score is a metric used to evaluate the performance of a classification model by
considering both precision and recall. Precision measures the accuracy of positive
predictions (i.e., the proportion of true positive predictions among all positive predictions
made by the model), while recall measures the model's ability to identify all relevant
positive instances (i.e., the proportion of true positive predictions among all actual positive
instances). The F1 score is the harmonic mean of precision and recall, providing a single
metric that balances both concerns. This is particularly useful when dealing with
imbalanced datasets or when the cost of false positives and false negatives is significant.
Options B, C, and D pertain to other aspects of model performance but are not related to
the F1 score.
Question # 6 Which strategy evaluates the accuracy of a foundation model (FM) that is used in image
classification tasks? A. Calculate the total cost of resources used by the model.B. Measure the model's accuracy against a predefined benchmark dataset.C. Count the number of layers in the neural network.D. Assess the color accuracy of images processed by the model.
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B. Measure the model's accuracy against a predefined benchmark dataset.
Answer Description Explanation:
Question # 7 A company is building an ML model to analyze archived data. The company must perform
inference on large datasets that are multiple GBs in size. The company does not need to
access the model predictions immediately. A. Batch transformB. Real-time inferenceC. Serverless inferenceD. Asynchronous inference
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A. Batch transformQuestion # 8 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.
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.
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B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
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