HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: regressionLinear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this component to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression
Box: validationA randomly extracted subset of data from a dataset is commonly used for ______ of the model.Samples from the original training dataset are split into the two subsets using random selection. This is to ensure that the train and test datasets are representative of the original dataset.
https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms
You have an Azure Machine Learning model that generates a large quantity of false negatives.You need to reduce the number of false negatives without re-training the model.What should you do?
Answer(s): C
What are different methods to minimize False Negatives and False Positives in Binary Classification? Threshold LineOne of the easiest methods to minimize the outcomes of a certain case is simply changing the decision boundary line from the basic 0.5 to above (when reducing False Positives) or below (when reducing False Negatives). It should be noted that by doing this, the possibility of False Positives increases. In other words, by decreasing the False Negatives we are increasing the False Positives.
https://www.kaggle.com/discussions/general/376229
What are three stages in a transformer model? Each correct answer presents a complete solution.Note: Each correct answer is worth one point.
Answer(s): B,C,D
D: What is tokenization in transformer?Preparing Text Data for Transformers: Tokenization, Mapping ...Tokenization is the process of dividing text into smaller units called tokens, which can be words, phrases, subwords, or characters. In the context of Transformer models, tokenization is a crucial step in preprocessing text data for use in natural language processing tasks.C: Input embeddings: The input sentence is first transformed into numerical representations called embeddings. These capture the semantic meaning of the tokens in the input sequence. For sequences of words, these embeddings can be learned during training or obtained from pre-trained word embeddings.B: Inference: After training, the model can be used for inference on new data. During inference, the input sequence is passed through the pre-trained model, and the model generates predictions or representations for the given task.
https://medium.com/@lokaregns/preparing-text-data-for-transformers-tokenization-mapping-and-padding- 9fbfbce28028https://www.ibm.com/topics/transformer-model
Extracting relationships between data from large volumes of unstructured data is an example of which type of AI workload?
Answer(s): B
Knowledge mining is the capability to extract information from large volumes of often unstructured data to create a searchable knowledge store.
https://www.linkedin.com/pulse/get-started-ai-azure-abhishek-shrivastava
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