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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?
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.
Which of the following algorithms is an example of unsupervised learning?
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
Which of the following is a type 1 error in statistical hypothesis testing?
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. Reference: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]
Which two of the following criteria are essential for machine learning models to achieve before deployment? (Select two.)
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment. Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions. Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.
Why do data skews happen in the ML pipeline?
Data skews happen in the ML pipeline when the distribution or characteristics of the live input data differ from those of the offline data used for training and testing the model. This can lead to a degradation of the model performance and accuracy, as the model is not able to generalize well to new data. Data skews can be caused by various factors, such as changes in user behavior, data collection methods, data quality issues, or external events. Reference:What is training-serving skew in Machine Learning?,Data preprocessing for ML: options and recommendations
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?
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 .
Exam domains verified against: Official CertNexus AIP-210 exam guide, last checked October 2026.
Focus on how AI and ML solve business problems across commercial, government, and research domains. Start by identifying the right ML algorithm for a use case and evaluating its success probability before diving into specific learning systems like image recognition, NLP, and recommendation engines.
Sample question from this domain above: Q1
Data quality and quantity directly affect algorithm performance. Work through the full data pipeline from collection and transformation through handling different data types and formats, applying standardization, normalization, and other transformations while managing ethical and business risks.
Design models using appropriate ML and deep learning algorithms, optimize structure and hyperparameters, and evaluate results using properly split train, validation, and test datasets. Balance performance gains against business risks and ethical considerations throughout this phase.
Sample question from this domain above: Q3
Take models from development to production by deploying them, securing the pipeline, and maintaining performance over time. This domain covers the largest portion of the exam and includes monitoring for drift, implementing MLOps practices, and embedding responsible AI principles in operations.
2.1 Explain the Cisco device boot-up process; 2.2 Identify common Cisco IOS commands; 2.3 Identify tools for device file management; 2.4 Confirm physical layer connectivity; 2.5 Access devices remotely over a network; 2.5.a Common Windows tools; 2.6 Explain how to connect to the console port; 2.7 Describe how to capture device status; 2.8 Describe techniques for password recovery; 2.9 Identify common tools for device replacement; 2.10 Locate serial numbers on Cisco devices
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