Predicting agricultural yields based on weather conditions and soil quality measurements is an example of which type of machine learning model?
Answer(s): B
Regression is a supervised machine learning technique used to predict numeric values.Note: Regression is a form of machine learning used to understand the relationships between variables to predict a desired outcome. Regression predicts a numeric label or outcome based on variables, or features.For example, an automobile sales company might use the characteristics of a car (such as engine size, number of seats, mileage, and so on) to predict its likely selling price. In this case, the characteristics of the car are the features, and the selling price is the label.Regression is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label. Then, after training has been completed, you can use the trained model to predict labels for new items for which the label is unknown.
https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning- designer/2-regression-scenarios
You have a dataset that contains experimental data for fuel samples.You need to predict the amount of energy in kilojoules that can be obtained from a sample based on its measured density.Which type of AI workload should you use?
Answer(s): D
Regression is a supervised machine learning technique which is used to predict continuous values. The ultimate goal of the regression algorithm is to plot a best-fit line or a curve between the data. The three main metrics that are used for evaluating the trained regression model are variance, bias and error.
https://builtin.com/data-science/regression-machine-learning
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: model trainingFor _____, you use a portion of a dataset to prepare a machine learning model and retain the balance of the dataset to verify the results.Machine learning models depend on data. Without a foundation of high-quality training data, even the most performant algorithms can be rendered useless.Training data refers to the initial data that is used to develop a machine learning model, from which the model creates and refines its rules. The quality of this data has profound implications for the model's subsequent development, setting a powerful precedent for all future applications that use the same training data.Incorrect:* feature engineeringThis process attempts to create additional relevant features from the existing raw features in the data, and to increase predictive power to the learning algorithm.
https://www.cloudfactory.com/training-data-guide
You have a dataset that contains sales data and has defined labels for types of customers.You need to categorize the customer types based on the sales data.Which type of machine learning should you use?
Classification is used with labeled data and is geared towards supervised learning, while clustering is used with unlabeled data, and geared towards unsupervised learning.
https://www.explorium.ai/blog/machine-learning/clustering-when-you-should-use-it-and-avoid-it
HOTSPOT (Drag and Drop is not supported)For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: YesYes - A transformer model architecture uses self-attention.By applying self-attention, the transformer model can capture the dependencies between different words in the input sequence and learn to focus on the most relevant words for each position. This helps in understanding the context and improving the quality of translation or any other sequence-based task.Box 2: YesYes - A transformer model architecture includes an encoder block and a decoder block.Transformer: Encoder-DecoderThe transformer encoder-decoder architecture is used for tasks like language translation, where the model must take in a sentence in one language and output a sentence in another language. The encoder takes in the input sentence and produces a fixed-size vector representation of it, which is then fed into the decoder to generate the output sentence. The decoder uses both self-attention and cross-attention, where the attention mechanism is applied to the output of the encoder and the input of the decoder.Box 3: NoNo - A transformer model architecture includes an encryption block and a decryption block.
https://medium.com/@minh.hoque/a-comprehensive-overview-of-transformer-based-models-encoders- decoders-and-more-e9bc0644a4e5
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