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[General] Boost Your Exam Prep With Dumpleader CertNexus AIP-210 Questions

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【General】 Boost Your Exam Prep With Dumpleader CertNexus AIP-210 Questions

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 2
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 3
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 4
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 5
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q49-Q54):NEW QUESTION # 49
Which of the following tools would you use to create a natural language processing application?
  • A. NLTK
  • B. Azure Search
  • C. AWS DeepRacer
  • D. DeepDream
Answer: A
Explanation:
Explanation
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .

NEW QUESTION # 50
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
  • A. Complexity
  • B. Explainability
  • C. Portability
  • D. Data size
  • E. Scalability
Answer: B,E
Explanation:
Explanation
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.

NEW QUESTION # 51
What is the primary benefit of the Federated Learning approach to machine learning?
  • A. It does not require a labeled dataset to solve supervised learning problems.
  • B. It requires less computation to train the same model using a traditional approach.
  • C. It uses large, centralized data stores to train complex machine learning models.
  • D. It protects the privacy of the user's data while providing well-trained models.
Answer: D
Explanation:
Federated learning is a distributed approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data with each other or a central server. This protects the privacy of the user's data while still enabling well-trained models that can benefit from diverse and large-scale datasets.
References: [Federated Learning - Wikipedia], [Federated Learning for Mobile Keyboard Prediction - Google AI Blog]

NEW QUESTION # 52
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
  • A. Recover from hidden attacks
  • B. Respond to hidden attacks
  • C. Prevent hidden attacks
  • D. Detect hidden attacks
Answer: C
Explanation:
Explanation
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model's performance that may indicate a potential attack.

NEW QUESTION # 53
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?
  • A. Logistic regression
  • B. Random forest
  • C. Decision tree
  • D. XGBoost
Answer: B

NEW QUESTION # 54
......
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