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[General] Valid Test AIP-210 Testking - AIP-210 Passed

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【General】 Valid Test AIP-210 Testking - AIP-210 Passed

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

AIP-210 Passed | Latest AIP-210 Test AnswersWhen you take CertNexus AIP-210 practice exams again and again you get familiar with the CertNexus Certified Artificial Intelligence Practitioner (CAIP) (AIP-210) real test pressure and learn to handle it for better outcomes. Features of the web-based and desktop AIP-210 Practice Exams are similar. The only difference is that the CertNexus Certified Artificial Intelligence Practitioner (CAIP) (AIP-210) web-based version works online.
CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q19-Q24):NEW QUESTION # 19
Which of the following pieces of AI technology provides the ability to create fake videos?
  • A. Generative adversarial networks (GAN)
  • B. Long short-term memory (LSTM) networks
  • C. Support-vector machines (SVM)
  • D. Recurrent neural networks (RNN)
Answer: A
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 # 20
Normalization is the transformation of features:
  • A. So that they are on a similar scale.
  • B. By subtracting from the mean and dividing by the standard deviation.
  • C. Into the normal distribution.
  • D. To different scales from each other.
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 # 21
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?
  • A. Preexisting
  • B. Emergent
  • C. Technical
  • D. Automation
Answer: A
Explanation:
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.

NEW QUESTION # 22
Which of the following is the definition of accuracy?
  • A. (True Positives + True Negatives) / Total Predictions
  • B. True Positives / (True Positives + False Negatives)
  • C. (True Positives + False Positives) / Total Predictions
  • D. True Positives / (True Positives + False Positives)
Answer: A
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).

NEW QUESTION # 23
Which of the following is the primary purpose of hyperparameter optimization?
  • A. Increases recall over precision
  • B. Controls the learning process of a given algorithm
  • C. Improves model interpretability
  • D. Makes models easier to explain to business stakeholders
Answer: B
Explanation:
Explanation
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.

NEW QUESTION # 24
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