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NEW QUESTION # 16
What is the primary benefit of the Federated Learning approach to machine learning?
Answer: B
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 # 17
Which of the following principles supports building an ML system with a Privacy by Design methodology?
Answer: C
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 # 18
Which of the following metrics is being captured when performing principal component analysis?
Answer: A
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 # 19
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.)
Answer: A,D
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 # 20
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
Answer: A
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 # 21
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