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[General] Quiz 2026 Marvelous CertNexus AIP-210: Test CertNexus Certified Artificial Intel

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【General】 Quiz 2026 Marvelous CertNexus AIP-210: Test CertNexus Certified Artificial Intel

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

Free Download Test AIP-210 Questions | Valid Latest AIP-210 Version: CertNexus Certified Artificial Intelligence Practitioner (CAIP)Our key priority is to provide such authentic CertNexus AIP-210 Exam Material which helps the candidate qualify for CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 exam on the very first attempt. This means that you can download the product right after purchasing and start your journey toward your big career.
CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q34-Q39):NEW QUESTION # 34
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

  • A. 37/(37+7)
  • B. (48+37)/100
  • C. 48/(48+37)
  • D. 37/(37+8)
Answer: D
Explanation:
Explanation
Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases).
Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.

NEW QUESTION # 35
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: A
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 # 36
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
  • A. K-nearest neighbors
  • B. K-means
  • C. Ridge regression
  • D. Lasso regression
  • E. Logistic regression
Answer: C,D
Explanation:
Explanation
Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]

NEW QUESTION # 37
A product manager is designing an Artificial Intelligence (AI) solution and wants to do so responsibly, evaluating both positive and negative outcomes.
The team creates a shared taxonomy of potential negative impacts and conducts an assessment along vectors such as severity, impact, frequency, and likelihood.
Which modeling technique does this team use?
  • A. Harms
  • B. Business
  • C. Threat
  • D. Process
Answer: A
Explanation:
Explanation
Harms modeling is a technique that helps product managers design AI solutions responsibly by evaluating both positive and negative outcomes. Harms modeling involves creating a shared taxonomy of potential negative impacts and conducting an assessment along vectors such as severity, impact, frequency, and likelihood. Harms modeling can help identify and mitigate any risks or harms that may arise from using AI solutions. References: [Harms Modeling for Responsible AI | by Google Developers | Google Developers],
[Harms Modeling for Responsible AI - YouTube]

NEW QUESTION # 38
Which of the following can take a question in natural language and return a precise answer to the question?
  • A. IBM Watson
  • B. Spark ML
  • C. Databricks
  • D. Pandas
Answer: A
Explanation:
Explanation
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.

NEW QUESTION # 39
......
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