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CertNexus AIP-210 Exam Syllabus Topics:

Topic	Details
Topic 1	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Topic 2	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Topic 3	Design machine and deep learning models Explain data collection transformation process in ML workflow
Topic 4	Transform numerical and categorical data Address business risks, ethical concerns, and related concepts in operationalizing the model
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	Identify potential ethical concerns Analyze machine learning system use cases

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q15-Q20):

NEW QUESTION # 15
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 # 16
Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)

A. The Beta value is the rate of type I errors for the test.
B. The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.
C. The Beta value is the rate of type II errors for the test.
D. The statistical power of a test is the inverse of the Beta value, or 1 - Beta.

Answer: C

Explanation:
Explanation
The Beta value in an A/B test is the probability of making a type II error, which is failing to reject the null hypothesis when it is false. The statistical power of a test is the probability of correctly rejecting the null hypothesis when it is false, which is equal to 1 - Beta. References: Formulas for Bayesian A/B Testing - Evan Miller, The Practical Guide To AB testing statistics | Convertize

NEW QUESTION # 17
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.

NEW QUESTION # 18
Which of the following statements are true regarding highly interpretable models? (Select two.)

A. They are usually binary classifiers.
B. They are usually easier to explain to business stakeholders.
C. They are usually referred to as "black box" models.
D. They usually compromise on model accuracy for the sake of interpretability.
E. They are usually very good at solving non-linear problems.

Answer: B,D

Explanation:
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
* They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
* They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.

NEW QUESTION # 19
Which of the following is NOT an activation function?

A. Additive
B. Hyperbolic tangent
C. Sigmoid
D. ReLU

Answer: A

Explanation:
An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:
* Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.
* Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.
* ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.
Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.

NEW QUESTION # 20
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AIP-210 test braindumps & AIP-210 exam questions & AIP-210 exam guide

CertNexus AIP-210 Exam Syllabus Topics:

Pass Guaranteed Accurate CertNexus - AIP-210 - Test CertNexus Certified Artificial Intelligence Practitioner (CAIP) Assessment

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q15-Q20):

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