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NEW QUESTION # 52
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Support-vector machines (SVM)
- B. Generative adversarial networks (GAN)
- C. Recurrent neural networks (RNN)
- D. Long short-term memory (LSTM) networks
Answer: B
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 # 53
An AI system recommends New Year's resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?
- A. When data drift is detected
- B. Periodically every year
- C. When concept drift is detected
- D. Periodically before New Year's Day and after New Year's Day
Answer: B
Explanation:
Explanation
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year's resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year's resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system's recommendations are up-to-date and relevant for each new year.
NEW QUESTION # 54
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Complexity
- B. Scalability
- C. Data size
- D. Explainability
- E. Portability
Answer: B,D
Explanation:
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.
NEW QUESTION # 55
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Word2Vec algorithm
- B. Bag of bigrams (2 letter pairs)
- C. Clustering similar words and representing words by group membership
- D. Bag of words model with TF-IDF
Answer: B
Explanation:
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 .
NEW QUESTION # 56
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?
- A. XGBoost
- B. Decision tree
- C. Random forest
- D. Logistic regression
Answer: C
Explanation:
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.
NEW QUESTION # 57
The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?
- A. 37/(37+8)
- B. 48/(48+37)
- C. (48+37)/100
- D. 37/(37+7)
Answer: A
Explanation:
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.
NEW QUESTION # 58
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
- A. Prevent hidden attacks
- B. Respond to hidden attacks
- C. Recover from hidden attacks
- D. Detect hidden attacks
Answer: A
Explanation:
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.
NEW QUESTION # 59
Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)
- A. The statistical power of a test is the inverse of the Beta value, or 1 - Beta.
- B. The Beta value is the rate of type I errors for the test.
- C. The Beta value is the rate of type II errors for the test.
- D. The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.
Answer: C
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 # 60 
The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
Explanation:
Explanation
The optimal value of K is the one that minimizes the inertia or within-cluster sum of squares, while avoiding too many clusters that may overfit the data. The elbow plot shows a sharp decrease in inertia from K = 1 to K
= 2, and then a more gradual decrease from K = 2 to K = 3. After K = 3, the inertia does not change much as K increases. Therefore, the elbow point is at K = 3, which is the optimal value of K for this data. References:
How to Run K-Means Clustering in Python, K-means clustering - Wikipedia
NEW QUESTION # 61
In a self-driving car company, ML engineers want to develop a model for dynamic pathing. Which of following approaches would be optimal for this task?
- A. Unsupervised Learning
- B. Reinforcement learning
- C. Supervised Learning.
- D. Dijkstra Algorithm
Answer: B
Explanation:
Explanation
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment.
Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .
NEW QUESTION # 62
Which of the following is the correct definition of the quality criteria that describes completeness?
- A. The degree to which the measures conform to defined business rules or constraints.
- B. The degree to which a set of measures are equivalent across systems.
- C. The degree to which all required measures are known.
- D. The degree to which a set of measures are specified using the same units of measure in all systems.
Answer: C
Explanation:
Explanation
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.
NEW QUESTION # 63
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 uses large, centralized data stores to train complex machine learning models.
- D. It protects the privacy of the user's data while providing well-trained models.
Answer: D
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 # 64
Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?
- A. Once a month
- B. When the input format changes
- C. When the input volume changes
- D. As new resources become available
Answer: B
Explanation:
Explanation
The input format is the way that the data is structured, organized, and presented to the model. For example, the input format could be a CSV file, an image file, or a JSON object. The input format can affect how the model interprets and processes the data, and therefore how it makes predictions. When the input format changes, it may require retraining the model to adapt to the new format and ensure its accuracy and reliability. For example, if the weather prediction application switches from using numerical values to categorical values for some features, such as wind direction or cloud cover, it may need to retrain the model to handle these changes
.
NEW QUESTION # 65
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Linear regression
- B. Polynomial regression
- C. Ridge regression
- D. Clustering
Answer: C
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 # 66
In general, models that perform their tasks:
- A. More accurately are less robust against adversarial attacks.
- B. Less accurately are neither more nor less robust against adversarial attacks.
- C. More accurately are neither more nor less robust against adversarial attacks.
- D. Less accurately are less robust against adversarial attacks.
Answer: A
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 # 67
When should you use semi-supervised learning? (Select two.)
- A. Labeling data is challenging and expensive.
- B. There is a large amount of labeled data to be used for predictions.
- C. A small set of labeled data is biased toward one class.
- D. There is a large amount of unlabeled data to be used for predictions.
- E. A small set of labeled data is available but not representative of the entire distribution.
Answer: A,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 # 68
Which of the following principles supports building an ML system with a Privacy by Design methodology?
- A. Understanding, documenting, and displaying data lineage.
- B. Collecting and processing the largest amount of data possible.
- C. Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
- D. Avoiding mechanisms to explain and justify automated decisions.
Answer: A
Explanation:
Explanation
Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data. By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3 .
NEW QUESTION # 69
Which of the following is NOT a valid cross-validation method?
- A. Bootstrapping
- B. K-fold
- C. Leave-one-out
- D. Stratification
Answer: D
Explanation:
Explanation
Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.
NEW QUESTION # 70
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. K-nearest neighbors
- B. Ridge regression
- C. Lasso regression
- D. K-means
- E. Logistic regression
Answer: B,C
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 # 71
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. Harms
- B. Process
- C. Business
- D. Threat
Answer: A
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 # 72
In which of the following scenarios is lasso regression preferable over ridge regression?
- A. The sample size is much larger than the number of features.
- B. The number of features is much larger than the sample size.
- C. There is high collinearity among some of the features associated with the dependent variable.
- D. There are many features with no association with the dependent variable.
Answer: D
Explanation:
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.
NEW QUESTION # 73
Which of the following metrics is being captured when performing principal component analysis?
- A. Variance
- B. Skewness
- C. Missingness
- D. Kurtosis
Answer: A
Explanation:
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.
NEW QUESTION # 74
A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?
- A. Recall and explained variance
- B. Precision and recall
- C. Mean squared error
- D. Precision and accuracy
Answer: B
Explanation:
Explanation
Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).
NEW QUESTION # 75
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