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NEW QUESTION # 70
A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.
Which evaluation metric meets these requirements?
Answer: D
Explanation:
Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.
C is correct:
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide)
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide) A (Recall) measures how many actual frauds are caught, but does not minimize false positives.
B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).
NEW QUESTION # 71
A company is developing an ML model to support the company's retail application. The company wants to use information that the model has produced from previous tasks to increase the learning speed of the model.
Which model training solution will meet these requirements?
Answer: B
Explanation:
Transfer learning is a machine learning technique that reuses knowledge learned from previous tasks to improve training efficiency and performance on new tasks. AWS documentation explains that transfer learning allows models to start from pretrained weights or representations, reducing training time and the amount of data required.
In this retail application scenario, the company wants to leverage information from prior tasks to increase learning speed, which is a defining characteristic of transfer learning. AWS emphasizes that transfer learning is especially effective when tasks are related, such as customer behavior analysis, product recommendations, or demand forecasting.
By initializing a model with learned features from an existing task, transfer learning enables faster convergence and improved accuracy compared to training from scratch. AWS frequently recommends this approach when computational efficiency and rapid iteration are important.
The other options do not satisfy the requirement. Supervised learning defines how labels are used but does not reuse prior knowledge. Hyperparameter tuning optimizes model configuration but does not leverage previous task outputs. Regularization techniques reduce overfitting but do not accelerate learning through knowledge reuse.
AWS documentation positions transfer learning as a foundational concept in modern ML workflows, particularly for retail, personalization, and natural language processing use cases. Therefore, transfer learning is the correct solution.
NEW QUESTION # 72
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 73
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
Answer: B
Explanation:
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.
* Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.
* Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.
* Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine- tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.
* Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.
AWS AI Practitioner References:
* Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.
NEW QUESTION # 74
An ecommerce company is developing an AI application that categorizes product images and extracts specifications. The application will use a high-quality labeled dataset to customize a foundation model (FM) to generate accurate responses.
Which ML technique will meet these requirements by using Amazon Bedrock?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The correct technique is fine-tuning, which is explicitly supported by Amazon Bedrock for customizing foundation models using high-quality labeled datasets.
Fine-tuning involves:
Starting with a pre-trained foundation model
Training it further using domain-specific, labeled data
Improving accuracy for specialized tasks, such as product classification, image-based understanding, and specification extraction In this use case:
The company has labeled data
They want to customize model behavior
They require high accuracy and domain adaptation
These conditions match the definition of fine-tuning, not prompt-only methods.
Why the other options are incorrect:
A). Continued pre-training typically requires massive unlabeled datasets and is not the standard customization method exposed in Amazon Bedrock.
B). Creating an agent orchestrates model interactions and tools but does not customize the model's learned parameters.
D). Prompt engineering improves responses through prompt design but does not modify the underlying model weights, making it insufficient for deep domain adaptation.
AWS AI document references (for exact extracts):
Amazon Bedrock Documentation - section on Model customization and fine-tuning AWS Generative AI Study Guide - comparison of prompt engineering vs fine-tuning Foundation Models on AWS - explanation of fine-tuning with labeled datasets
NEW QUESTION # 75
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