Quiz Fantastic AIF-C01 - Latest AWS Certified AI Practitioner Study Materials
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- Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
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| Topic 2 |
- Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
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| Topic 3 |
- Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
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| Topic 4 |
- Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
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| Topic 5 |
- Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
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Amazon AWS Certified AI Practitioner Sample Questions (Q43-Q48):
NEW QUESTION # 43
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?
- A. Residual neural network
- B. Generative adversarial network (GAN)
- C. XGBoost
- D. WaveNet
Answer: B
Explanation:
Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.
Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.
Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.
Option C: "Residual neural network" is primarily used for improving the performance of deep networks, not for generating synthetic data.
Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.
AWS AI Practitioner Reference:
GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.
NEW QUESTION # 44
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?
- A. Amazon SageMaker JumpStart
- B. Amazon Personalize
- C. Amazon SageMaker endpoints
- D. PartyRock, an Amazon Bedrock Playground
Answer: C
NEW QUESTION # 45
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.
Which business objective should the company use to evaluate the effect of the LLM chatbot?
- A. Website engagement rate
- B. Average call duration
- C. Corporate social responsibility
- D. Regulatory compliance
Answer: B
NEW QUESTION # 46
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?
- A. Use unsupervised learning to create a model that will estimate probability density.
- B. Use supervised learning to create a regression model that will predict probability.
- C. Use code that will calculate probability by using simple rules and computations.
- D. Use reinforcement learning to train a model to return the probability.
Answer: C
NEW QUESTION # 47
A company is building an ML model to analyze archived dat
a. 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.
Which Amazon SageMaker inference option will meet these requirements?
- A. Serverless inference
- B. Batch transform
- C. Real-time inference
- D. Asynchronous inference
Answer: B
Explanation:
Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.
Option A (Correct): "Batch transform": This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.
Option B: "Real-time inference" is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.
Option C: "Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.
Option D: "Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.
AWS AI Practitioner Reference:
Batch Transform on AWS SageMaker: AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.
NEW QUESTION # 48
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