A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

1. A. Supervised learning.
2. B. Unsupervised learning.
3. C. Reinforcement learning.
4. D. Active learning.

Correct Answer: A
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?

1. A. Adjust the prompt.
2. B. Choose an LLM of a different size.
3. C. Increase the temperature.
4. D. Increase the Top K value.

Correct Answer: A
Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.
✑ Adjust the Prompt:
✑ Why Option A is Correct:
✑ Why Other Options are Incorrect:

A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

1. A. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
2. B. Increase the model's complexity by adding more layers to the model's architecture.
3. C. Create effective prompts that provide clear instructions and context to guide the model's generation.
4. D. Select a large, diverse dataset to pre-train a new generative model.

Correct Answer: C
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.
✑ Effective Prompt Engineering:
✑ Why Option C is Correct:
✑ Why Other Options are Incorrect:

Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

1. A. Embeddings
2. B. Tokens
3. C. Models
4. D. Binaries

Correct Answer: A
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
✑ Option A (Correct): "Embeddings": This is the correct term, as embeddings provide
a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
✑ Option B: "Tokens" are pieces of text used in processing, but they do not capture
semantic meanings like embeddings do.
✑ Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
✑ Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
✑ Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.

A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?

1. A. Provide labeled data with the prompt field and the completion field.
2. B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
3. C. Purchase Provisioned Throughput for Amazon Bedrock.
4. D. Train the model on journals and textbooks.

Correct Answer: A
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
✑ Fine-Tuning Strategy:
✑ Why Option A is Correct:
✑ Why Other Options are Incorrect: