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[Hardware] 2026 Efficient CertNexus AIP-210: Exam CertNexus Certified Artificial Intelligen

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【Hardware】 2026 Efficient CertNexus AIP-210: Exam CertNexus Certified Artificial Intelligen

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CertNexus AIP-210 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Understanding the Artificial Intelligence Problem
  • Analyze the use cases of ML algorithms to rank them by their success probability
Topic 2
  • Identify potential ethical concerns
  • Analyze machine learning system use cases
Topic 3
  • Train, validate, and test data subsets
  • Training and Tuning ML Systems and Models
Topic 4
  • Recognize relative impact of data quality and size to algorithms
  • Engineering Features for Machine Learning

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q34-Q39):NEW QUESTION # 34
Which two encodes can be used to transform categories data into numerical features? (Select two.)
  • A. Count Encoder
  • B. Log Encoder
  • C. One-Hot Encoder
  • D. Mean Encoder
  • E. Median Encoder
Answer: C,D
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 # 35
Which of the following is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data?
  • A. Sarbanes Oxley (SOX)
  • B. PCIDSS
  • C. ISO/IEC 27001
  • D. General Data Protection Regulation (GDPR)
Answer: D
Explanation:
The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.

NEW QUESTION # 36
Which of the following scenarios is an example of entanglement in ML pipelines?
  • A. Change in normalization function in the feature engineering step.
  • B. Add a new pipeline for retraining the model in the model training step.
  • C. Change the way output is visualized in the monitoring step.
  • D. Add a new method for drift detection in the model evaluation step.
Answer: A
Explanation:
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it.
Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.

NEW QUESTION # 37
A data scientist is tasked to extract business intelligence from primary data captured from the public. Which of the following is the most important aspect that the scientist cannot forget to include?
  • A. Cyberprotection
  • B. Data security
  • C. Cybersecurity
  • D. Data privacy
Answer: D
Explanation:
Data privacy is the right of individuals to control how their personal data is collected, used, shared, and protected. It also involves complying with relevant laws and regulations that govern the handling of personal data. Data privacy is especially important when extracting business intelligence from primary data captured from the public, as it may contain sensitive or confidential information that could harm the individuals if misused or breached .

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. Emergent
  • B. Technical
  • C. Automation
  • D. Preexisting
Answer: D
Explanation:
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
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