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[Hardware] Prep AIP-210 Guide, Practice AIP-210 Test Engine

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【Hardware】 Prep AIP-210 Guide, Practice AIP-210 Test Engine

Posted at yesterday 19:20      View：3 | Replies：0        Print      Only Author   [Copy Link] 1#
P.S. Free 2026 CertNexus AIP-210 dumps are available on Google Drive shared by Getcertkey: https://drive.google.com/open?id=1dj4sRcUHJ0JBjPDZz-8vb7H9E-AUSDOb
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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
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 3
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q38-Q43):NEW QUESTION # 38
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 protects the privacy of the user's data while providing well-trained models.
  • C. It uses large, centralized data stores to train complex machine learning models.
  • D. It requires less computation to train the same model using a traditional approach.
Answer: B
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 # 39
Which of the following is the correct definition of the quality criteria that describes completeness?
  • A. The degree to which a set of measures are specified using the same units of measure in all systems.
  • B. The degree to which all required measures are known.
  • C. The degree to which the measures conform to defined business rules or constraints.
  • D. The degree to which a set of measures are equivalent across systems.
Answer: B
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 # 40
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
  • A. The training data used was inaccurate.
  • B. The team set flawed expectations when training the model.
  • C. The AI model was trained in winter and applied in summer.
  • D. The application was migrated from on-premise to a public cloud.
Answer: C
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.

NEW QUESTION # 41
Which of the following pieces of AI technology provides the ability to create fake videos?
  • A. Long short-term memory (LSTM) networks
  • B. Support-vector machines (SVM)
  • C. Generative adversarial networks (GAN)
  • D. Recurrent neural networks (RNN)
Answer: C
Explanation:
Explanation
Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.

NEW QUESTION # 42
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?
  • A. Logistic regression
  • B. k-nearest neighbors
  • C. k-means clustering
  • D. Naive-Bayes
Answer: C
Explanation:
Explanation
k-means clustering is an algorithm that should be used by the market research team to obtain groups of patients with similar unmet needs. k-means clustering is an unsupervised learning technique that partitions the data into k clusters based on the similarity of the features. The algorithm iteratively assigns each data point to the cluster with the nearest centroid and updates the centroid until convergence. k-means clustering can help identify patterns and segments in the data that may not be obvious or intuitive. References: [K-means clustering - Wikipedia], [How to Run K-Means Clustering in Python]

NEW QUESTION # 43
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P.S. Free 2026 CertNexus AIP-210 dumps are available on Google Drive shared by Getcertkey: https://drive.google.com/open?id=1dj4sRcUHJ0JBjPDZz-8vb7H9E-AUSDOb
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