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NEW QUESTION: 1
Examine this SQL statement:

Which two are true?
A. The subquery is executed for every row in the employees table.
B. The subquery is not a correlated subquery.
C. All existing rows in the EMPLOYEES table are deleted.
D. The DELETE statement executes successfully even if the subquery selects multiple rows.
E. The subquery is executed before the DELETE statement is executed.
Answer: A,E

NEW QUESTION: 2
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 a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a MimicExplainer.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.
Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

NEW QUESTION: 3


Answer:
Explanation:

Explanation

NEW QUESTION: 4
HOTSPOT
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training dat
a. You plan to automate the process of adding more data to the training models and to constantly tune the models by using the additional data, including data that is collected in near real-time. The system will be used to analyze event data gathered from many different sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from Facebook, which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types for the various tasks associated to the processing pipeline.
You are using Microsoft Power BI Desktop to create visualizations of the crime data predictions. You connect to a JSON file that is stored in Azure Blob storage.
After loading the data into Power BI, the query shows the following metadata fields only:
Name
Content
Extension
Date created
Date accessed
Date modified
The actual columns have the following names:
Duration
Zip Code
Start Time
Probability
Crime Type
Likelihood Percent
You need to transform the query so that Power BI can access the actual columns rather than the metadata.
Which two actions should you perform from the Edit Queries menu on the Home ribbon? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

References: https://social.technet.microsoft.com/wiki/contents/articles/37512.create-power-bi- reports-from-json-data-exposed-by-rest-service.aspx#Convert_JSON_to_table_data