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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.
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
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.
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?
A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
C. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
D. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?
A. Pairs of chatbot responses and correct user intents
B. Pairs of user messages and correct chatbot responses
C. Pairs of user messages and correct user intents
D. Pairs of user intents and correct chatbot responses
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.
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