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AIP-210시험패스가능한공부문제 - AIP-210시험대비최신버전덤프

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CertNexus AIP-210 시험요강:

주제
소개

주제 1

• Design machine and deep learning models
• Explain data collection
• transformation process in ML workflow

주제 2

• Recognize relative impact of data quality and size to algorithms
• Engineering Features for Machine Learning

주제 3

• Transform numerical and categorical data
• Address business risks, ethical concerns, and related concepts in operationalizing the model

주제 4

• Understanding the Artificial Intelligence Problem
• Analyze the use cases of ML algorithms to rank them by their success probability

 

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CertNexus AIP-210 최신덤프

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최신 Certified AI Practitioner AIP-210 무료샘플문제 (Q39-Q44):

질문 # 39
Which of the following sentences is true about model evaluation and model validation in ML pipelines?

• A. Model evaluation and validation are the same.
• B. Model validation is defined as a set of tasks to confirm the model performs as expected.
• C. Model validation occurs before model evaluation.
• D. Model evaluation is defined as an external component.

정답：B

설명：
Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics. References: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning

 

질문 # 40
Which of the following are true about the transform-design pattern for a machine learning pipeline? (Select three.) It aims to separate inputs from features.

• A. It represents steps in the pipeline with a directed acyclic graph (DAG).
• B. It ensures reproducibility.
• C. It seeks to isolate individual steps of ML pipelines.
• D. It transforms the output data after production.
• E. It encapsulates the processing steps of ML pipelines.

정답：B,C,E

설명：
The transform-design pattern for ML pipelines aims to separate inputs from features, encapsulate the processing steps of ML pipelines, and represent steps in the pipeline with a DAG. These goals help to make the pipeline modular, reusable, and easy to understand. The transform-design pattern does not seek to isolate individual steps of ML pipelines, as this would create entanglement and dependency issues. It also does not transform the output data after production, as this would violate the principle of separation of concerns.

 

질문 # 41
Which of the following describes a benefit of machine learning for solving business problems?

• A. Improving the quality of original data
• B. Increasing the quantity of original data
• C. Increasing the speed of analysis
• D. Improving the constraint of the problem

정답：C

설명：
Increasing the speed of analysis is a benefit of machine learning for solving business problems. Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and make predictions or decisions. Machine learning can help increase the speed of analysis by automating and optimizing various tasks, such as data processing, feature extraction, model training, model evaluation, or model deployment. Machine learning can also help handle large and complex data sets that may be difficult or impractical to analyze manually or with traditional methods.

 

질문 # 42
Which of the following tests should be performed at the production level before deploying a newly retrained model?

• A. Performance test
• B. Security test
• C. A/Btest
• D. Unit test

정답：A

설명：
Explanation
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. References: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]

 

질문 # 43
Which of the following is NOT a valid cross-validation method?

• A. K-fold
• B. Bootstrapping
• C. Stratification
• D. Leave-one-out

정답：C

설명：
Explanation
Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.

 

질문 # 44
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