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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| Topic 2: AI Application Development | 30% | - Implement AI applications using AWS services
|
| Topic 3: Responsible AI | 20% | - Explain the principles of responsible AI
|
| Topic 4: AWS AI Services Overview | 26% | - Describe Amazon SageMaker capabilities
|
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NEW QUESTION # 25
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?
Answer: D
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 Reference:
Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.
NEW QUESTION # 26
A company stores its AI datasets in Amazon S3 buckets. The company wants to share the S3 buckets with its business partners. The company needs to avoid accidentally sharing sensitive data.
Which AWS service should the company use to discover sensitive data in the dataset?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon Macie uses machine learning to:
* Discover sensitive data such as PII
* Classify data stored in Amazon S3
* Help prevent unintended data exposure
AWS security guidance recommends Macie before data sharing to ensure compliance and privacy protection.
Why the other options are incorrect:
* Kendra (A) is a search service.
* Textract (C) extracts text from documents.
* Data Exchange (D) shares datasets, not analyzes sensitivity.
AWS AI document references:
* Amazon Macie Overview
* Protecting Sensitive Data in S3
* Data Privacy and Governance on AWS
NEW QUESTION # 27
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
Answer: A
Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.
Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.
Option B: "Model customization" is a feature, not a pricing model.
Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.
Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.
AWS AI Practitioner Reference:
AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.
NEW QUESTION # 28
A company wants to keep its foundation model (FM) relevant by using the most recent dat a. The company wants to implement a model training strategy that includes regular updates to the FM.
Which solution meets these requirements?
Answer: A
Explanation:
To keep a foundation model (FM) relevant with the most recent data, the company needs a training strategy that supports regular updates. Continuous pre-training involves periodically updating a pre-trained model with new data to improve its performance and relevance over time, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Continuous pre-training is a strategy where a pre-trained model is periodically updated with new data to keep it relevant and improve its performance. This approach is commonly used for foundation models to ensure they adapt to new trends and information." (Source: AWS AI Practitioner Learning Path, Module on Model Training Strategies) Detailed Option A: Batch learningBatch learning involves training a model on a fixed dataset in batches, but it does not inherently support regular updates with new data to keep the model relevant over time.
Option B: Continuous pre-trainingThis is the correct answer. Continuous pre-training updates the FM with recent data, ensuring it stays relevant by adapting to new trends and information.
Option C: Static trainingStatic training implies training a model once on a fixed dataset without updates, which does not meet the requirement for regular updates.
Option D: Latent trainingLatent training is not a standard term in AWS or ML contexts. It may refer to latent space in models like VAEs, but it is not a strategy for regular model updates.
Reference:
AWS AI Practitioner Learning Path: Module on Model Training Strategies
Amazon Bedrock User Guide: Model Customization and Updates (https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html) AWS Documentation: Machine Learning Training Strategies (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 29
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
Answer: C
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner Reference:
Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
NEW QUESTION # 30
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