Question # 1
You need to implement a model development strategy to determine a user’s tendency to respond to an ad.
Which technique should you use? | A. Use a Relative Expression Split module to partition the data based on centroid distance. | B. Use a Relative Expression Split module to partition the data based on distance travelled to the event. | C. Use a Split Rows module to partition the data based on distance travelled to the event. | D. Use a Split Rows module to partition the data based on centroid distance. |
A. Use a Relative Expression Split module to partition the data based on centroid distance.
Explanation:
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent
References:
https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data
Question # 2
You are building a machine learning model for translating English language textual content into French
language textual content.
You need to build and train the machine learning model to learn the sequence of the textual content.
Which type of neural network should you use? | A. Multilayer Perceptions (MLPs) | B. Convolutional Neural Networks (CNNs) | C. Recurrent Neural Networks (RNNs) | D. Generative Adversarial Networks (GANs) |
C. Recurrent Neural Networks (RNNs)
Explanation:
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both.
They’re called recurrent because the network’s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
Question # 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
| A. Yes | B. No |
B. No
Question # 4
You use Azure Machine Learning to train a model based on a dataset named dataset1.
You define a dataset monitor and create a dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.
Which method of the DataDriftDetector class should you use? | A. run | B. get | C. backfill | D. update |
C. backfill
Explanation:
A backfill run is used to see how data changes over time.
[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector, , ]
Question # 5
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do? | A. Apply an analysis of variance (ANOVA). | B. Apply a Pearson correlation coefficient. | C. Apply a Spearman correlation coefficient. | D. Apply a linear discriminant analysis. |
D. Apply a linear discriminant analysis.
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Question # 6
You need to resolve the local machine learning pipeline performance issue. What should you do? | A. Increase Graphic Processing Units (GPUs). | B. Increase the learning rate. | C. Increase the training iterations, | D. Increase Central Processing Units (CPUs). |
A. Increase Graphic Processing Units (GPUs).
Question # 7
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.
Model performance has degraded due to model input data changes.
You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.
Which event should you use? | A. RunStatusChanged | B. DatasetDriftDetected | C. ModelDeployed | D. RunCompleted |
B. DatasetDriftDetected
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