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NEW QUESTION # 281
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
Explanation:
The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes
/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
Binary classification: Supervised learning
Multi-class classification: Supervised learning
K-means clustering: Unsupervised learning
Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)
NEW QUESTION # 282
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Answer: B
NEW QUESTION # 283
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?
Answer: B
Explanation:
Comprehensive and Detailed
ISO certifications apply to processes, frameworks, and systems - not individuals or every piece of software.
When a company is ISO-certified, its development framework and governance processes comply with ISO standards for security, risk, or AI responsibility.
Reference:
AWS Compliance Programs - ISO
NEW QUESTION # 284
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 # 285
A company has fine-tuned an Amazon Bedrock foundation model (FM) to produce short document summaries. The company wants an automated metric that compares each model-generated summary with its human-written reference summary.
Which metric will meet these requirements?
Answer: B
Explanation:
AWS documentation identifies ROUGE as a standard evaluation metric for text summarization tasks. ROUGE measures the overlap of n-grams, sequences, or word pairs between a machine-generated summary and a human-written reference summary, making it ideal for automated evaluation.
In this scenario, the company needs a metric that directly compares generated summaries with human references. ROUGE scores quantify how much of the reference content is captured by the model output, which aligns with AWS recommendations for evaluating summarization quality.
The other metrics are not appropriate. F1 score is typically used for classification tasks. Perplexity measures language model confidence but does not compare outputs to reference summaries. Frechet Inception Distance is used for image generation tasks, not text.
AWS consistently recommends ROUGE for summarization evaluation, making it the correct choice.
NEW QUESTION # 286
......
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