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NEW QUESTION # 152
Which scenario indicates that an ML model is overfitting?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
Overfitting occurs when an ML model learns patterns too closely from training data, including noise, and fails to generalize to unseen data. According to AWS ML fundamentals, a key indicator of overfitting is high training accuracy combined with reduced accuracy on new or test data.
Option A directly reflects this behavior: the model performs well during training but loses accuracy when evaluated on new data, which is a textbook symptom of overfitting.
Why the other options are not overfitting:
B describes an under-featured model (underfitting risk).
C indicates poor data representativeness, not overfitting.
D reflects limited feature selection, which may lead to underfitting or bias, not overfitting.
AWS AI Study Guide Reference:
AWS Machine Learning concepts: bias vs. variance
AWS training and evaluation best practices
NEW QUESTION # 153
Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all. (Select THREE.)
* Chain-of-thought prompting
* Few-shot prompting
* Role-based prompting
* Single-shot prompting
* Zero-shot prompting
Answer:
Explanation:
Explanation:
The verified selections are Few-shot prompting , Chain-of-thought prompting , and Zero-shot prompting . The first description matches few-shot prompting because AWS describes few-shot prompting as a technique that includes example outputs or demonstrations in the initial prompt so the model can understand the expected pattern before generating a response. The phrase "provide a small number of examples" is the key indicator.
A few-shot prompt gives the model limited examples of the desired task, format, or reasoning style, and the model uses those examples as context for the next output.
The second description matches chain-of-thought prompting . AWS describes chain-of-thought prompting as a technique that helps a model solve a problem by following a series of intermediate reasoning steps before reaching the final answer. The wording "break down the step-by-step process" directly points to chain-of- thought prompting. This method is commonly associated with reasoning, arithmetic, logic, planning, and multi-step problem solving because the prompt encourages the model to work through intermediate steps rather than immediately outputting a final answer.
The third description matches zero-shot prompting because AWS describes zero-shot prompting as asking the model to perform a task without providing examples in the prompt. The model relies only on the instruction and its pre-trained knowledge. The phrase "without providing examples" is the decisive clue.
Role-based prompting is not used here because none of the descriptions asks the model to act as a specific persona, job role, or domain expert, such as "Act as a financial analyst" or "You are a security engineer." Single-shot prompting is also not used because the first description says a "small number of examples," which indicates few-shot prompting, not exactly one example. Therefore, the three correct hotspot mappings are Few-shot prompting , Chain-of-thought prompting , and Zero-shot prompting .
NEW QUESTION # 154
A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?
Answer: D
Explanation:
The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.
From AWS Bedrock documentation:
"When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences." Explanation of other options:
A). Model size affects performance and cost but doesn't directly guarantee low latency.
B). Training data quality is important for accuracy, but it doesn't address real-time performance requirements.
C). GPU availability matters in infrastructure planning, not in model selection for latency optimization.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Selection Guide - Real-time Use Case Considerations
* AWS ML Specialty Guide - Foundation Model Performance Criteria
NEW QUESTION # 155
A company wants to learn about generative AI applications in an experimental environment.
Which solution will meet this requirement MOST cost-effectively?
Answer: B
Explanation:
The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge workers. None of those are as cost-effective and experimental-focused as PartyRock.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Documentation - PartyRock Overview
AWS Generative AI Learning Path - Getting Started Tools
NEW QUESTION # 156
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?
Answer: A
Explanation:
The correct answer is B because Amazon SageMaker JumpStart provides pre-built solutions, including training workflows for popular open-source LLMs such as Falcon, LLaMA, and others. It allows practitioners to quickly launch fine-tuning jobs using predefined templates, minimizing operational setup and code complexity.
From AWS documentation:
"Amazon SageMaker JumpStart enables you to fine-tune and deploy foundation models with minimal setup. It provides easy-to-use interfaces and pre-built configurations for training, which significantly reduces the operational overhead required to train models." Explanation of other options:
A . PartyRock is designed for prototyping generative AI apps but does not support model training or fine-tuning.
C . Writing a custom script for SageMaker training is flexible but involves more operational effort, including handling infrastructure configuration.
D Training on EC2 via a Jupyter notebook is fully manual and operationally intensive, including dependency setup, data handling, and resource scaling.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker JumpStart Developer Guide - Fine-tuning Foundation Models AWS Certified Machine Learning Specialty Guide - Model Customization and JumpStart
NEW QUESTION # 157
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