Question # 1
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? | | A. Fine-tune the model by using additional training data that is representative of the various
age ranges that the application will support. | | B. Add a role description to the prompt context that instructs the model of the age range
that the response should target. | | C. Use chain-of-thought reasoning to deduce the correct style and complexity for a
response suitable for that user. | | D. Summarize the response text depending on the age of the user so that younger users
receive shorter responses. |
B. Add a role description to the prompt context that instructs the model of the age range
that the response should target.
Question # 2
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? | | A. On-Demand | | B. Model customization | | C. Provisioned Throughput | | D. Spot Instance |
A. On-Demand
Question # 3
| Which option is a use case for generative AI models? | | A. Improving network security by using intrusion detection systems | | B. Creating photorealistic images from text descriptions for digital marketing | | C. Enhancing database performance by using optimized indexing | | D. Analyzing financial data to forecast stock market trends |
B. Creating photorealistic images from text descriptions for digital marketing
Explanation:
Generative AI models are used to create new content based on existing data. One
common use case is generating photorealistic images from text descriptions, which is
particularly useful in digital marketing, where visual content is key to engaging potential
customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital
marketing": This is the correct answer because generative AI models, like those
offered by Amazon Bedrock, can create images based on text descriptions,
making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is
incorrect because this is a use case for traditional machine learning models, not
generative AI.
Option C: "Enhancing database performance by using optimized indexing" is
incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect
because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
Use Cases for Generative AI Models on AWS: AWS highlights the use of
generative AI for creative content generation, including image creation, text
generation, and more, which is suited for digital marketing applications.
Question # 4
A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs? | | A. Amazon EC2 C series | | B. Amazon EC2 G series
| | C. Amazon EC2 P series
| | D. Amazon EC2 Trn series
|
D. Amazon EC2 Trn series
Question # 5
A company wants to use language models to create an application for inference on edge
devices. The inference must have the lowest latency possible.
Which solution will meet these requirements? | | A. Deploy optimized small language models (SLMs) on edge devices. | | B. Deploy optimized large language models (LLMs) on edge devices. | | C. Incorporate a centralized small language model (SLM) API for asynchronous
communication with edge devices. | | D. Incorporate a centralized large language model (LLM) API for asynchronous
communication with edge devices. |
A. Deploy optimized small language models (SLMs) on edge devices.
Question # 6
| What are tokens in the context of generative AI models? | | A. Tokens are the basic units of input and output that a generative AI model operates on,
representing words, subwords, or other linguistic units. | | B. Tokens are the mathematical representations of words or concepts used in generative
AI models. | | C. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for
specific tasks. | | D. Tokens are the specific prompts or instructions given to a generative AI model to
generate output. |
A. Tokens are the basic units of input and output that a generative AI model operates on,
representing words, subwords, or other linguistic units.
Question # 7
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. Generative adversarial network (GAN) | | B. XGBoost | | C. Residual neural network | | D. WaveNet |
A. Generative adversarial network (GAN)
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 References:
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.
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