(Oct-2023) Latest AIP-210 Dumps for Success in Actual CertNexus Certified [Q15-Q39]

Published date October 16, 2023

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(Oct-2023) Latest AIP-210 Dumps for Success in Actual CertNexus Certified

Changing the Concept of AIP-210 Exam Preparation 2023

CertNexus AIP-210 Exam Syllabus Topics:

Topic	Details
Topic 1	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Topic 2	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability
Topic 3	Identify potential ethical concerns Analyze machine learning system use cases
Topic 4	Transform numerical and categorical data Address business risks, ethical concerns, and related concepts in operationalizing the model
Topic 5	Address business risks, ethical concerns, and related concepts in training and tuning Work with textual, numerical, audio, or video data formats

NO.15 Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?

A more complex model

Guaranteed availability of enough space

Increase in data bandwidth consumption

Reduction in latency

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.

NO.16 Which type of regression represents the following formula: y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable?

Lasso regression

Linear regression

Polynomial regression

Ridge regression

NO.17 An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.
Which ethical principle is this an example of?

Fairness

Privacy

Safety

Transparency

Explanation
Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system’s actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system’s recommendations, as well as facilitate auditing and feedback.

NO.18 Which of the following metrics is being captured when performing principal component analysis?

Kurtosis

Missingness

Skewness

Variance

Explanation
Principal component analysis (PCA) is a technique that reduces the dimensionality of a dataset by transforming it into a set of new variables called principal components. The principal components are linear combinations of the original variables that capture the maximum amount of variance in the data. The first principal component explains the most variance, the second principal component explains the second most variance, and so on. The goal of PCA is to retain as much variance as possible while reducing the number of variables.

NO.19 When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

Bag of words model with TF-IDF

Bag of bigrams (2 letter pairs)

Word2Vec algorithm

Clustering similar words and representing words by group membership

Explanation
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word “hello” would be represented as
{“he”: 1, “el”: 1, “ll”: 1, “lo”: 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as “th” in English or “ch” in German .

NO.20 For a particular classification problem, you are tasked with determining the best algorithm among SVM, random forest, K-nearest neighbors, and a deep neural network. Each of the algorithms has similar accuracy on your data. The stakeholders indicate that they need a model that can convey each feature’s relative contribution to the model’s accuracy. Which is the best algorithm for this use case?

Deep neural network

K-nearest neighbors

Random forest

SVM

Explanation
Random forest is an ensemble learning method that combines multiple decision trees to create a more accurate and robust classifier or regressor. Random forest can convey each feature’s relative contribution to the model’s accuracy by measuring how much the prediction error increases when a feature is randomly permuted. This metric is called feature importance or Gini importance. Random forest can also provide insights into the interactions and dependencies among features by visualizing the decision trees .

NO.21 Normalization is the transformation of features:

By subtracting from the mean and dividing by the standard deviation.

Into the normal distribution.

So that they are on a similar scale.

To different scales from each other.

Explanation
Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or
-1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k-nearest neighbors. References: [Feature scaling – Wikipedia], [Normalization vs Standardization – Quantitative analysis]

NO.22 When should the model be retrained in the ML pipeline?

A new monitoring component is added.

Concept drift is detected in the pipeline.

More data become available for the training phase.

Some outliers are detected in live data.

Explanation
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer – Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing

NO.23 Which of the following algorithms is an example of unsupervised learning?

Neural networks

Principal components analysis

Random forest

Ridge regression

Explanation
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.

NO.24 A healthcare company experiences a cyberattack, where the hackers were able to reverse-engineer a dataset to break confidentiality.
Which of the following is TRUE regarding the dataset parameters?

The model is overfitted and trained on a high quantity of patient records.

The model is overfitted and trained on a low quantity of patient records.

The model is underfitted and trained on a high quantity of patient records.

The model is underfitted and trained on a low quantity of patient records.

Explanation
Overfitting is a problem that occurs when a model learns too much from the training data and fails to generalize well to new or unseen data. Overfitting can result from using a low quantity of training data, a high complexity of the model, or a lack of regularization. Overfitting can also increase the risk of reverse-engineering a dataset from a model’s outputs, as the model may reveal too much information about the specific features or patterns of the training data. This can break the confidentiality of the data and expose sensitive information about the individuals in the dataset .

NO.25 In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?

Detect hidden attacks

Prevent hidden attacks

Recover from hidden attacks

Respond to hidden attacks

Explanation
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model’s performance that may indicate a potential attack.

NO.26 Which of the following is the primary purpose of hyperparameter optimization?

Controls the learning process of a given algorithm

Makes models easier to explain to business stakeholders

Improves model interpretability

Increases recall over precision

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.

NO.27 A change in the relationship between the target variable and input features is

concept drift.

covariate shift.

data drift.

model decay.

Explanation
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam. References: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift

NO.28 Word Embedding describes a task in natural language processing (NLP) where:

Words are converted into numerical vectors.

Words are featurized by taking a histogram of letter counts.

Words are featurized by taking a matrix of bigram counts.

Words are grouped together into clusters and then represented by word cluster membership.

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.

NO.29 Which of the following unsupervised learning models can a bank use for fraud detection?

Anomaly detection

DB5CAN

Hierarchical clustering

k-means

Explanation
Anomaly detection is an unsupervised learning technique that identifies outliers or abnormal patterns in data, which can be useful for fraud detection. Anomaly detection algorithms can learn the normal behavior of transactions and flag the ones that deviate significantly from the norm, indicating possible fraud.

NO.30 Which of the following describes a typical use case of video tracking?

Augmented dreaming

Medical diagnosis

Traffic monitoring

Video composition

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.

NO.31 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?

Cyberprotection

Cybersecurity

Data privacy

Data security

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 .

NO.32 You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

Decision tree

Logistic regression

Random forest

XGBoost

Explanation
Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.

NO.33 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?

General Data Protection Regulation (GDPR)

ISO/IEC 27001

PCIDSS

Sarbanes Oxley (SOX)

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.

NO.34 Which of the following equations best represent an LI norm?

|x| + |y|

|x|+|y|^2

|x|-|y|

|x|^2+|y|^2

Explanation
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.

NO.35 The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

48/(48+37)

37/(37+8)

37/(37+7)

(48+37)/100

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.

NO.36 Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)

Complexity

Data size

Explainability

Portability

Scalability

Explanation
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.

NO.37 For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the
10-year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?

Equality of variance (Homoscedastidty)

Independence

Linearity

Normality

Explanation
Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors.
Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.

NO.38 Which two encodes can be used to transform categories data into numerical features? (Select two.)

Count Encoder

Log Encoder

Mean Encoder

Median Encoder

One-Hot Encoder

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.

NO.39 In which of the following scenarios is lasso regression preferable over ridge regression?

The number of features is much larger than the sample size.

There are many features with no association with the dependent variable.

There is high collinearity among some of the features associated with the dependent variable.

The sample size is much larger than the number of features.

Explanation
Lasso regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of the coefficients to zero, which effectively eliminates some of the features from the model. Lasso regression is preferable over ridge regression when there are many features with no association with the dependent variable, as it can perform feature selection and reduce the complexity and noise of the model.

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