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[General] Pass Leader CertNexus AIP-210 Dumps - AIP-210 Questions Answers

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【General】 Pass Leader CertNexus AIP-210 Dumps - AIP-210 Questions Answers

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 2
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 3
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 4
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 5
  • Address business risks, ethical concerns, and related concepts in training and tuning
  • Work with textual, numerical, audio, or video data formats
Topic 6
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q25-Q30):NEW QUESTION # 25
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
  • A. Model validation is defined as a set of tasks to confirm the model performs as expected.
  • B. Model evaluation and validation are the same.
  • C. Model validation occurs before model evaluation.
  • D. Model evaluation is defined as an external component.
Answer: A
Explanation:
Explanation
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

NEW QUESTION # 26
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
  • A. Configuration Management
  • B. Incident
  • C. Code Repository
  • D. Asset
Answer: B
Explanation:
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.

NEW QUESTION # 27
In general, models that perform their tasks:
  • A. Less accurately are neither more nor less robust against adversarial attacks.
  • B. More accurately are less robust against adversarial attacks.
  • C. More accurately are neither more nor less robust against adversarial attacks.
  • D. Less accurately are less robust against adversarial attacks.
Answer: B
Explanation:
Explanation
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]

NEW QUESTION # 28

The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: B
Explanation:
The optimal value of K is the one that minimizes the inertia or within-cluster sum of squares, while avoiding too many clusters that may overfit the data. The elbow plot shows a sharp decrease in inertia from K = 1 to K
= 2, and then a more gradual decrease from K = 2 to K = 3. After K = 3, the inertia does not change much as K increases. Therefore, the elbow point is at K = 3, which is the optimal value of K for this data. References:
How to Run K-Means Clustering in Python, K-means clustering - Wikipedia

NEW QUESTION # 29
In a self-driving car company, ML engineers want to develop a model for dynamic pathing. Which of following approaches would be optimal for this task?
  • A. Supervised Learning.
  • B. Unsupervised Learning
  • C. Dijkstra Algorithm
  • D. Reinforcement learning
Answer: D
Explanation:
Explanation
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment.
Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

NEW QUESTION # 30
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