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[General] Instant AIP-210 Discount, New AIP-210 Test Materials

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【General】 Instant AIP-210 Discount, New AIP-210 Test Materials

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q72-Q77):NEW QUESTION # 72
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?
  • A. Get more training data
  • B. Increase the complexity of the model
  • C. Train the model for more epochs
  • D. Add features to training data
Answer: A
Explanation:
Explanation
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.

NEW QUESTION # 73
Which two encodes can be used to transform categories data into numerical features? (Select two.)
  • A. Log Encoder
  • B. Median Encoder
  • C. Count Encoder
  • D. Mean Encoder
  • E. One-Hot Encoder
Answer: D,E
Explanation:
Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models. Some of the encoding methods that can be used to transform categorical data into numerical features are:
* Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems.
* One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.

NEW QUESTION # 74
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 a set of measures are equivalent across systems.
  • D. The degree to which the measures conform to defined business rules or constraints.
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 # 75
Which of the following is a type 1 error in statistical hypothesis testing?
  • A. The null hypothesis is true and fails to be rejected.
  • B. The null hypothesis is false and is rejected.
  • C. The null hypothesis is false, but fails to be rejected.
  • D. The null hypothesis is true, but is rejected.
Answer: D
Explanation:
A type 1 error in statistical hypothesis testing is when the null hypothesis is true, but is rejected. This means that the test falsely concludes that there is a significant difference or effect when there is none. The probability of making a type 1 error is denoted by alpha, which is also known as the significance level of the test. A type 1 error can be reduced by choosing a smaller alpha value, but this may increase the chance of making a type 2 error, which is when the null hypothesis is false but fails to be rejected. References: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]

NEW QUESTION # 76
Which of the following pieces of AI technology provides the ability to create fake videos?
  • A. Long short-term memory (LSTM) networks
  • B. Generative adversarial networks (GAN)
  • C. Support-vector machines (SVM)
  • D. Recurrent neural networks (RNN)
Answer: B
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 # 77
......
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