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You use Azure Machine Learning designer to build a model pipeline.
What should you create before you can run the pipeline?
- A. a registered model
- B. a compute resource
- C. a Jupyter notebook
DRAG DROP
–
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
You need to create a customer support solution to help customers access information. The solution must support email, phone, and live chat channels.
Which type of Al solution should you use?
- A. machine learning
- B. computer vision
- C. chatbot
- D. natural language processing (NLP)
DRAG DROP
–
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Predicting how many vehicles will travel across a bridge on a give day is an example of _______.
Select the answer that correctly completes the sentence.
- A. regression
- B. translation
- C. classification
- D. clustering
In a machine learning model, the data that is used as inputs are called ________.
Select the answer that correctly completes the sentence.
- A. dataset
- B. labels
- C. variables
HOTSPOT
–
Select the answer that correctly completes the sentence.
DRAG DROP
–
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
HOTSPOT
–
Select the answer that correctly completes the sentence.
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer.
What should you do?
- A. Split the original dataset into a dataset for training and a dataset for testing. Use the testing dataset for evaluation.
- B. Use the original dataset for training and evaluation.
- C. Split the original dataset into a dataset for features and a dataset for labels. Use the features dataset for evaluation.
- D. Split the original dataset into a dataset for training and a dataset for testing. Use the training dataset for evaluation.