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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q292-Q297):

NEW QUESTION # 292
Hotspot Question
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.
You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:
Tabular Explainer: Used with tabular datasets.
Text Explainer: Used with text datasets.
Image Explainer: Used with image datasets.
Box 1: Tabular
Box 2: Text
Box 3: Image
Incorrect Answers:
Hierarchical Attention Network (HAN)
HAN was proposed by Yang et al. in 2016. Key features of HAN that differentiates itself from existing approaches to document classification are (1) it exploits the hierarchical nature of text data and (2) attention mechanism is adapted for document classification.
Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning- d07975741298

NEW QUESTION # 293
You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset

NEW QUESTION # 294
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 train and register an Azure Machine Learning model.
You plan to deploy the model to an online end point.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a Kubernetes online endpoint and set the value of its auth-mode parameter to amyl Token. Deploy the model to the online endpoint.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 295
You are using a Git repository to track work in an Azure Machine Learning workspace.
You need to authenticate a Git account by using SSH.
Which three 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.

Answer:

Explanation:

Authenticate your Git Account with SSH:
Step 1: Generating a public/private key pair
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C "your_email@example.com"
This creates a new ssh key, using the provided email as a label.
> Generating public/private rsa key pair.
Step 2: Add the public key to the Git Account
In your terminal window, copy the contents of your public key file.
Step 3: Clone the Git repository by using an SSH repository URL
1. Copy the SSH Git clone URL from the Git repo.
2. Paste the url into the git clone command below, to use your SSH Git repo URL. This will look something like:
git clone git@example.com:GitUser/azureml-example.git
Cloning into 'azureml-example'.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration

NEW QUESTION # 296
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values:
* learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the search space for the Hyperdrive experiment.
Which two parameter expressions should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. a uniform expression for learning_rate
B. a uniform expression for batch_size
C. a choice expression for batch_size
D. a normal expression for batch_size
E. a choice expression for learning_rate

Answer: A,C

Explanation:
B: Continuous hyperparameters are specified as a distribution over a continuous range of values. Supported distributions include:
uniform(low, high) - Returns a value uniformly distributed between low and high D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
one or more comma-separated values
a range object
any arbitrary list object
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
Topic 1, Overview
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment Requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

NEW QUESTION # 297
......

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