[May-2026] The CertNexus AIP-210 Exam Test For Brief Preparation
Revolutionary Guide To Exam CertNexus Dumps
NEW QUESTION # 32
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
- A. If the data is only collected once.
- B. If it was requested by the authority of law.
- C. If it is for a good cause.
- D. If it was collected accidentally.
- E. If it was with consent of the person it is collected from.
Answer: B,E
Explanation:
Explanation
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.
NEW QUESTION # 33
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It requires less computation to train the same model using a traditional approach.
- B. It does not require a labeled dataset to solve supervised learning problems.
- C. It protects the privacy of the user's data while providing well-trained models.
- D. It uses large, centralized data stores to train complex machine learning models.
Answer: C
Explanation:
Explanation
Federated learning is a distributed approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data with each other or a central server. This protects the privacy of the user's data while still enabling well-trained models that can benefit from diverse and large-scale datasets.
References: [Federated Learning - Wikipedia], [Federated Learning for Mobile Keyboard Prediction - Google AI Blog]
NEW QUESTION # 34
Why do data skews happen in the ML pipeline?
- A. There Is a mismatch between live input data and offline data.
- B. There is insufficient training data for evaluation.
- C. Test and evaluation data are designed incorrectly.
- D. There is a mismatch between live output data and offline data.
Answer: A
Explanation:
Explanation
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. References: What is training-serving skew in Machine Learning?, Data preprocessing for ML: options and recommendations
NEW QUESTION # 35
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Stacking
- B. Weighted class
- C. Bagging
- D. Boosting
Answer: B
Explanation:
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog
NEW QUESTION # 36
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. General Data Protection Regulation (GDPR)
- B. Sarbanes Oxley (SOX)
- C. ISO/IEC 27001
- D. PCIDSS
Answer: A
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 # 37
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?
- A. k-means, because this is a clustering problem with a small number of features.
- B. Neural network model, because this is a classification problem with a large number of features.
- C. Logistic regression, because this is a classification problem and our data is linearly separable.
- D. A decision tree algorithm, because the problem is a classification problem with a small number of features.
Answer: B
Explanation:
Explanation
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. References: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]
NEW QUESTION # 38
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. A more complex model
- B. Reduction in latency
- C. Guaranteed availability of enough space
- D. Increase in data bandwidth consumption
Answer: B
Explanation:
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.
NEW QUESTION # 39
Which of the following is the primary purpose of hyperparameter optimization?
- A. Controls the learning process of a given algorithm
- B. Increases recall over precision
- C. Improves model interpretability
- D. Makes models easier to explain to business stakeholders
Answer: A
Explanation:
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.
NEW QUESTION # 40
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. Pandas
- D. Databricks
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 # 41
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?
- A. Low false negative rate
- B. High negative predictive value
- C. High positive predictive value
- D. Low false positive rate
Answer: A
Explanation:
Explanation
A false negative is an error where a positive case (belonging to the target class) is incorrectly predicted as negative (not belonging to the target class). A false negative rate is the ratio of false negatives to all actual positive cases. A low false negative rate means that most of the positive cases are correctly identified by the classifier.
For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can receive treatment, it is of primary importance that it has a low false negative rate. This is because false negatives can have serious consequences for patients who have the disease but do not receive treatment, such as increased risk of mortality or complications. A low false negative rate can ensure that most patients who have the disease are diagnosed correctly and receive timely treatment.
NEW QUESTION # 42
Which of the following tools would you use to create a natural language processing application?
- A. DeepDream
- B. NLTK
- C. AWS DeepRacer
- D. Azure Search
Answer: B
Explanation:
Explanation
NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .
NEW QUESTION # 43
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Support-vector machines (SVM)
- B. Recurrent neural networks (RNN)
- C. Generative adversarial networks (GAN)
- D. Long short-term memory (LSTM) networks
Answer: C
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 # 44
Which of the following scenarios is an example of entanglement in ML pipelines?
- A. Add a new method for drift detection in the model evaluation step.
- B. Change the way output is visualized in the monitoring step.
- C. Add a new pipeline for retraining the model in the model training step.
- D. Change in normalization function in the feature engineering step.
Answer: D
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 # 45
You create a prediction model with 96% accuracy. While the model's true positive rate (TPR) is performing well at 99%, the true negative rate (TNR) is only 50%. Your supervisor tells you that the TNR needs to be higher, even if it decreases the TPR. Upon further inspection, you notice that the vast majority of your data is truly positive.
What method could help address your issue?
- A. Principal components analysis
- B. Oversampling
- C. Quality filtering
- D. Normalization
Answer: B
Explanation:
Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate. References: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis - Wikipedia]
NEW QUESTION # 46
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Clustering
- B. Polynomial regression
- C. Linear regression
- D. Ridge regression
Answer: D
Explanation:
Explanation
Ridge regression is a type of regularization technique that can help reduce collinearity among independent variables. It does this by adding a penalty term to the ordinary least squares (OLS) objective function, which shrinks the coefficients of highly correlated variables towards zero. This reduces the variance of the coefficient estimates and improves the stability and accuracy of the regression model. References: Multicollinearity in Regression Analysis: Problems, Detection, and Solutions - Statistics By Jim, A Beginner's Guide to Collinearity: What it is and How it affects our regression model - StrataScratch
NEW QUESTION # 47
Which of the following describes a typical use case of video tracking?
- A. Traffic monitoring
- B. Video composition
- C. Augmented dreaming
- D. Medical diagnosis
Answer: A
Explanation:
Video tracking is a technique that involves detecting and following moving objects in a video sequence.
Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human- computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.
NEW QUESTION # 48
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
- A. Sampling bias
- B. Stereotype bias
- C. Algorithmic bias
- D. Systematic value distortion
Answer: A
Explanation:
Explanation
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
NEW QUESTION # 49
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are featurized by taking a histogram of letter counts.
- B. Words are grouped together into clusters and then represented by word cluster membership.
- C. Words are featurized by taking a matrix of bigram counts.
- D. Words are converted into numerical vectors.
Answer: D
Explanation:
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
* One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
* Word2vec: Word2vec is a method that learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations.
* GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.
NEW QUESTION # 50
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A. Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- B. Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- C. Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
- D. Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
Answer: C
Explanation:
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
* Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
* Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
* Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
* Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.
NEW QUESTION # 51
Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)
- A. The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.
- B. The statistical power of a test is the inverse of the Beta value, or 1 - Beta.
- C. The Beta value is the rate of type I errors for the test.
- D. The Beta value is the rate of type II errors for the test.
Answer: D
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 # 52
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