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NEW QUESTION # 282
A company wants to create a new solution by using AWS Glue. The company has minimal programming experience with AWS Glue.
Which AWS service can help the company use AWS Glue?
Answer: D
Explanation:
AWS Glue is a serverless data integration service that enables users to extract, transform, and load (ETL) data. For a company with minimal programming experience, Amazon Q Developer provides an AI-powered assistant that can generate code, explain AWS services, and guide users through tasks like creating AWS Glue jobs. This makes it an ideal tool to help the company use AWS Glue effectively.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Q Developer:
"Amazon Q Developer is an AI-powered assistant that helps developers by generating code, answering questions about AWS services, and providing step-by-step guidance for tasks such as building ETL pipelines with AWS Glue. It is designed to assist users with varying levels of expertise, including those with minimal programming experience." (Source: AWS Documentation, Amazon Q Developer Overview) Detailed Explanation:
Option A: Amazon Q Developer
This is the correct answer. Amazon Q Developer can assist the company by generating AWS Glue scripts, explaining Glue concepts, and providing guidance on setting up ETL jobs, which is particularly helpful for users with limited programming experience.
Option B: AWS Config
AWS Config is used for tracking and managing resource configurations and compliance, not for assisting with coding or using services like AWS Glue. This option is incorrect.
Option C: Amazon Personalize
Amazon Personalize is a machine learning service for building recommendation systems, not for assisting with data integration or AWS Glue. This option is irrelevant.
Option D: Amazon Comprehend
Amazon Comprehend is an NLP service for analyzing text, not for helping users write code or use AWS Glue.
This option does not meet the requirements.
References:
AWS Documentation: Amazon Q Developer Overview (https://aws.amazon.com/q/developer/) AWS Glue Developer Guide: Introduction to AWS Glue (https://docs.aws.amazon.com/glue/latest/dg/what-is- glue.html) AWS AI Practitioner Learning Path: Module on AWS Developer Tools and Services
NEW QUESTION # 283
Sometimes generative AI models generate data unrelated to the input or the task.
Which term is used for this disadvantage of using generative AI for business problems?
Answer: A
Explanation:
AWS documentation identifies hallucinations as a known limitation of generative AI models, particularly when used in business and production environments. Hallucinations occur when a model generates outputs that are unrelated, incorrect, fabricated, or unsupported by the input data or provided context. These outputs often appear confident and fluent, which can make them difficult to detect without additional validation.
Generative AI models, including large language models, operate using probabilistic token prediction based on patterns learned during training. AWS explains that these models do not have true reasoning or factual grounding unless explicitly provided with context or external knowledge. As a result, when prompts are ambiguous, incomplete, or outside the model's training distribution, the model may produce responses that are irrelevant or misleading.
This behavior presents a risk for business use cases such as customer support, reporting, or decision-making systems. AWS highlights hallucinations as a key challenge that must be mitigated through techniques such as Retrieval Augmented Generation (RAG), prompt engineering, human review, and output validation.
The other options are not correct. Interpretability refers to the ability to understand model decisions, not incorrect outputs. Data bias relates to skewed or unfair training data. Nondeterminism refers to variability in outputs, not relevance or correctness.
AWS consistently categorizes hallucinations as a primary disadvantage of generative AI, making this the correct answer.
NEW QUESTION # 284
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Answer: A
Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
* Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
* Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
* Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
* Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner References:
* Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
NEW QUESTION # 285
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML
Answer:
Explanation:
NEW QUESTION # 286
Why does overfilting occur in ML models?
Answer: B
Explanation:
Overfitting occurs when an ML model learns the training data too well, including noise and patterns that do not generalize to new data. A key cause of overfitting is when the training dataset does not represent all possible input values, leading the model to over-specialize on the limited data it was trained on, failing to generalize to unseen data.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting often occurs when the training dataset is not representative of the broader population of possible inputs, causing the model to memorize specific patterns, including noise, rather than learning generalizable features." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Option A: The training dataset does not represent all possible input values.This is the correct answer. If the training dataset lacks diversity and does not cover the range of possible inputs, the model overfits by learning patterns specific to the training data, failing to generalize.
Option B: The model contains a regularization method.Regularization methods (e.g., L2 regularization) are used to prevent overfitting, not cause it. This option is incorrect.
Option C: The model training stops early because of an early stopping criterion.Early stopping is a technique to prevent overfitting by halting training when performance on a validation set degrades. It does not cause overfitting.
Option D: The training dataset contains too many features.While too many features can contribute to overfitting (e.g., by increasing model complexity), this is less directly tied to overfitting than a non-representative dataset. The dataset's representativeness is the primary cause.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 287
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