Question # 1
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
| A. Helps decrease the model's complexity
| B. Improves model performance over time
| C. Decreases the training time requirement
| D. Optimizes model inference time
|
B. Improves model performance over time
Explanation:
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
Ongoing Pre-Training:
Involves continuously training a model with new data to adapt to changing patterns, enhance generalization, and improve performance on specific tasks.
Helps the model stay updated with the latest data trends and minimize drift over time.
Why Option B is Correct:
Performance Enhancement:Continuously updating the model with new data improves its accuracy and relevance.
Adaptability:Ensures the model adapts to new data distributions or domain-specific nuances.
Why Other Options are Incorrect:
A. Decrease model complexity:Ongoing pre-training typically enhances complexity by learning new patterns, not reducing it.
C. Decreases training time requirement:Ongoing pre-training may increase the time needed for training.
D. Optimizes inference time:Does not directly affect inference time; rather, it affects model performance.
Question # 2
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements? | | A. Build a conversational chatbot by using Amazon Lex. | | B. Transcribe call recordings by using Amazon Transcribe. | | C. Extract information from call recordings by using Amazon SageMaker Model Monitor. | | D. Create classification labels by using Amazon Comprehend. |
B. Transcribe call recordings by using Amazon Transcribe.
Question # 3
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.
Which solution meets these requirements? | | A. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations. | | B. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information. | | C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations. | | D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades. |
C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
Question # 4
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements? | | A. Object detection | | B. Anomaly detection | | C. Named entity recognition | | D. Inpainting |
A. Object detection
Explanation:
Object detection is the correct strategy for automatically identifying and categorizing animals in photos.
Object Detection:
A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.
Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.
Why Option A is Correct:
Automatic Identification:Object detection models can automatically identify different types of animals in the images without manual intervention.
Categorization Capability:Assigns labels to detected objects, fulfilling the requirement for categorizing animals.
Why Other Options are Incorrect:
B. Anomaly detection:Identifies outliers or unusual patterns, not specific objects in images.
C. Named entity recognition:Used in NLP to identify entities in text, not for image processing.
D. Inpainting:Used for filling in missing parts of images, not for detecting or categorizing objects.
Question # 5
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements? | | A. Build an automatic named entity recognition system.
| | B. Create a recommendation engine.
| | C. Develop a summarization chatbot.
| | D. Develop a multi-language translation system.
|
C. Develop a summarization chatbot.
Question # 6
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 # 7
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). |
A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
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