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CertNexus AIP-210 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Transform numerical and categorical data
  • Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 2
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow
Topic 3
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 4
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
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 (Q51-Q56):

NEW QUESTION # 51
What is the primary benefit of the Federated Learning approach to machine learning?

Answer: A

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
Which of the following is the correct definition of the quality criteria that describes completeness?

Answer: A

Explanation:
Explanation
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.


NEW QUESTION # 53
Normalization is the transformation of features:

Answer: A

Explanation:
Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or
-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k- nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization - Quantitative analysis]


NEW QUESTION # 54
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)

Answer: A,D

Explanation:
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
* The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
* The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.


NEW QUESTION # 55
Which of the following approaches is best if a limited portion of your training data is labeled?

Answer: C

Explanation:
Explanation
Semi-supervised learning is an approach that is best if a limited portion of your training data is labeled.
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.


NEW QUESTION # 56
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