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[Hardware] New Released CertNexus AIP-210 Questions Verified by Experts [2026]

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【Hardware】 New Released CertNexus AIP-210 Questions Verified by Experts [2026]

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P.S. Free 2026 CertNexus AIP-210 dumps are available on Google Drive shared by Itcertking: https://drive.google.com/open?id=10Mfeg265TrlCXPCdXajrDLA3mGDZQhFh
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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
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning
Topic 3
  • Design machine and deep learning models
  • Explain data collection
  • transformation process in ML workflow

CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q35-Q40):NEW QUESTION # 35
We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?
  • A. Square-root transformation
  • B. Standardization
  • C. Log transformation
  • D. Normalization
Answer: D
Explanation:
Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.

NEW QUESTION # 36
Which of the following principles supports building an ML system with a Privacy by Design methodology?
  • A. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
  • B. Understanding, documenting, and displaying data lineage.
  • C. Collecting and processing the largest amount of data possible.
  • D. Avoiding mechanisms to explain and justify automated decisions.
Answer: B
Explanation:
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .

NEW QUESTION # 37
Which of the following is NOT an activation function?
  • A. Hyperbolic tangent
  • B. ReLU
  • C. Additive
  • D. Sigmoid
Answer: C
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 # 38
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. Technical
  • B. Automation
  • C. Emergent
  • D. Preexisting
Answer: D
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 # 39
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
  • A. After logit-transformation, the data may violate the assumption of independence.
  • B. Noisy data could become more influential in your model.
  • C. Values near 0.5 before logit-transformation will be near 0 after.
  • D. The model will be more likely to violate the assumption of normality.
Answer: B
Explanation:
Explanation
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients. References:
[Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]

NEW QUESTION # 40
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