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NEW QUESTION # 26
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
- A. The training data used was inaccurate.
- B. The team set flawed expectations when training the model.
- C. The AI model was trained in winter and applied in summer.
- D. The application was migrated from on-premise to a public cloud.
Answer: C
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.
NEW QUESTION # 27
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. Process
- B. Threat
- C. Harms
- D. Business
Answer: C
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 # 28
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Mean Encoder
- B. One-Hot Encoder
- C. Median Encoder
- D. Log Encoder
- E. Count Encoder
Answer: A,B
Explanation:
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 # 29
Which of the following is TRUE about SVM models?
- A. They can take the feature space into higher dimensions to solve the problem.
- B. They use the sigmoid function to classify the data points.
- C. They can be used only for classification.
- D. They can be used only for regression.
Answer: A
Explanation:
Explanation
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia
NEW QUESTION # 30
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
- A. Model evaluation is defined as an external component.
- B. Model validation is defined as a set of tasks to confirm the model performs as expected.
- C. Model validation occurs before model evaluation.
- D. Model evaluation and validation are the same.
Answer: B
Explanation:
Explanation
Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics. References: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning
NEW QUESTION # 31
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?
- A. Delete entire rows that contain any missing features.
- B. Fill in missing features with random values for that feature in the training set.
- C. Delete entire columns that contain any missing features.
- D. Fill in missing features with the average of observed values for that feature in the entire dataset.
Answer: D
Explanation:
Explanation
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.
NEW QUESTION # 32
Which of the following is NOT an activation function?
- A. Sigmoid
- B. Additive
- C. Hyperbolic tangent
- D. ReLU
Answer: B
Explanation:
Explanation
An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:
Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.
Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.
ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.
Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.
NEW QUESTION # 33
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It protects the privacy of the user's data while providing well-trained models.
- B. It requires less computation to train the same model using a traditional approach.
- C. It does not require a labeled dataset to solve supervised learning problems.
- D. It uses large, centralized data stores to train complex machine learning models.
Answer: A
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
Which of the following items should be included in a handover to the end user to enable them to use and run a trained model on their own system? (Select three.)
- A. Intermediate data files
- B. README document
- C. Sample input and output data files
- D. Information on the folder structure in your local machine
- E. Link to a GitHub repository of the codebase
Answer: B,C,E
Explanation:
Explanation
A handover is the process of transferring the ownership and responsibility of an ML system from one party to another, such as from the developers to the end users. A handover should include all the necessary information and resources that enable the end users to use and run a trained model on their own system. Some of the items that should be included in a handover are:
Link to a GitHub repository of the codebase: A GitHub repository is an online platform that hosts the source code and version control of an ML system. A link to a GitHub repository can provide the end users with access to the latest and most updated version of the codebase, as well as the history and documentation of the changes made to the code.
README document: A README document is a text file that provides an overview and instructions for an ML system. A README document can include information such as the purpose, features, requirements, installation, usage, testing, troubleshooting, and license of the system.
Sample input and output data files: Sample input and output data files are data files that contain examples of valid inputs and expected outputs for an ML system. Sample input and output data files can help the end users understand how to use and run the system, as well as verify its functionality and performance.
NEW QUESTION # 35
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They usually compromise on model accuracy for the sake of interpretability.
- B. They are usually binary classifiers.
- C. They are usually referred to as "black box" models.
- D. They are usually very good at solving non-linear problems.
- E. They are usually easier to explain to business stakeholders.
Answer: A,E
Explanation:
Explanation
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.
NEW QUESTION # 36
You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
- A. Random forest
- B. Ridge regression
- C. Support-vector machine
- D. Deep learning neural network
Answer: B
Explanation:
Explanation
Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.
NEW QUESTION # 37
In general, models that perform their tasks:
- A. More accurately are neither more nor less robust against adversarial attacks.
- B. More accurately are less robust against adversarial attacks.
- C. Less accurately are neither more nor less robust against adversarial attacks.
- D. Less accurately are less robust against adversarial attacks.
Answer: B
Explanation:
Explanation
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data. References: [Adversarial machine learning - Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
NEW QUESTION # 38
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Generative adversarial networks (GAN)
- B. Long short-term memory (LSTM) networks
- C. Support-vector machines (SVM)
- D. Recurrent neural networks (RNN)
Answer: A
Explanation:
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 # 39
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Ridge regression
- B. Linear regression
- C. Polynomial regression
- D. Clustering
Answer: A
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 # 40
When should you use semi-supervised learning? (Select two.)
- A. There is a large amount of labeled data to be used for predictions.
- B. There is a large amount of unlabeled data to be used for predictions.
- C. A small set of labeled data is biased toward one class.
- D. Labeling data is challenging and expensive.
- E. A small set of labeled data is available but not representative of the entire distribution.
Answer: B,D
Explanation:
Explanation
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance.
There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self-training, co-training, or generative models, to incorporate unlabeled data into the learning process.
NEW QUESTION # 41
You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
- A. When the distribution of the dependent variable is Gaussian.
- B. When the categories of the dependent variable are not linearly separable.
- C. When there is high correlation among the features.
- D. When it is necessary to save computational time.
Answer: B
Explanation:
Explanation
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.
NEW QUESTION # 42
Which of the following can take a question in natural language and return a precise answer to the question?
- A. Databricks
- B. IBM Watson
- C. Spark ML
- D. Pandas
Answer: B
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 # 43
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?
- A. Lasso regression
- B. Ridge regression
- C. Polynomial regression
- D. Linear regression
Answer: D
NEW QUESTION # 44
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?
- A. Linearity
- B. Independence
- C. Equality of variance (Homoscedastidty)
- D. Normality
Answer: B
Explanation:
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.
NEW QUESTION # 45
What is Word2vec?
- A. A bag of words.
- B. A matrix of how frequently words appear in a group of documents.
- C. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- D. A word embedding method that finds characteristics of words in a very large number of documents.
Answer: D
Explanation:
Explanation
Word2vec is a word embedding method that finds characteristics of words in a very large number of documents. Word embedding is a technique that converts words into numerical vectors that represent their meaning, usage, or context. Word2vec 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 associations1.
NEW QUESTION # 46
R-squared is a statistical measure that:
- A. Expresses the extent to which two variables are linearly related.
- B. Combines precision and recall of a classifier into a single metric by taking their harmonic mean.
- C. Is the proportion of the variance for a dependent variable thaf' s explained by independent variables.
- D. Represents the extent to which two random variables vary together.
Answer: C
Explanation:
Explanation
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.
NEW QUESTION # 47
Which two of the following decrease technical debt in ML systems? (Select two.)
- A. Model complexity
- B. Design anti-patterns
- C. Documentation readability
- D. Boundary erosion
- E. Refactoring
Answer: C,E
Explanation:
Explanation
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.
NEW QUESTION # 48
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