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NEW QUESTION # 21
In general, models that perform their tasks:
- A. More accurately are neither more nor less robust against adversarial attacks.
- B. Less accurately are less robust against adversarial attacks.
- C. Less accurately are neither more nor less robust against adversarial attacks.
- D. More accurately are less robust against adversarial attacks.
Answer: D
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 # 22
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 it was with consent of the person it is collected from.
- B. If it is for a good cause.
- C. If it was collected accidentally.
- D. If it was requested by the authority of law.
- E. If the data is only collected once.
Answer: A,D
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 # 23
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. Deep learning neural network
- B. Ridge regression
- C. Random forest
- D. Support-vector machine
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 # 24
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. Threat
- B. Business
- C. Process
- D. Harms
Answer: D
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 # 25
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Median Encoder
- B. One-Hot Encoder
- C. Log Encoder
- D. Count Encoder
- E. Mean Encoder
Answer: B,E
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 # 26
Which of the following is NOT an activation function?
- A. Additive
- B. Hyperbolic tangent
- C. Sigmoid
- D. ReLU
Answer: A
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 # 27
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?
- A. Transparency
- B. Fairness
- C. Safety
- D. Privacy
Answer: A
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.
NEW QUESTION # 28
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
- A. Values near 0.5 before logit-transformation will be near 0 after.
- B. The model will be more likely to violate the assumption of normality.
- C. After logit-transformation, the data may violate the assumption of independence.
- D. Noisy data could become more influential in your model.
Answer: D
Explanation:
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients.
References: [Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]
NEW QUESTION # 29
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
- A. Values near 0.5 before logit-transformation will be near 0 after.
- B. The model will be more likely to violate the assumption of normality.
- C. After logit-transformation, the data may violate the assumption of independence.
- D. Noisy data could become more influential in your model.
Answer: D
Explanation:
Explanation
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients. References:
[Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]
NEW QUESTION # 30
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?
- A. Transparency
- B. Fairness
- C. Safety
- D. Privacy
Answer: A
Explanation:
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.
NEW QUESTION # 31
Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?
- A. Add 1 to all of the Y values.
- B. Subtract the mean of Y from all the Y values.
- C. Explore the data for outliers.
- D. Divide all the Y values by the standard deviation of Y.
Answer: A
Explanation:
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.
NEW QUESTION # 32
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 columns that contain any missing features.
- B. Delete entire rows that contain any missing features.
- C. Fill in missing features with the average of observed values for that feature in the entire dataset.
- D. Fill in missing features with random values for that feature in the training set.
Answer: C
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 # 33
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
- A. Lasso regression
- B. Ridge regression
- C. K-nearest neighbors
- D. Logistic regression
- E. K-means
Answer: A,B
Explanation:
Explanation
Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]
NEW QUESTION # 34
Which of the following tools would you use to create a natural language processing application?
- A. Azure Search
- B. AWS DeepRacer
- C. NLTK
- D. DeepDream
Answer: C
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 # 35
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. Ridge regression
- B. Polynomial regression
- C. Linear regression
- D. Lasso regression
Answer: C
NEW QUESTION # 36
Which of the following scenarios is an example of entanglement in ML pipelines?
- A. Add a new pipeline for retraining the model in the model training step.
- B. Add a new method for drift detection in the model evaluation step.
- C. Change the way output is visualized in the monitoring step.
- D. Change in normalization function in the feature engineering step.
Answer: D
Explanation:
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
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