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NEW QUESTION: 1
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 a Microsoft Excel workbook that is saved to Microsoft SharePoint Online. The workbook contains several Power View sheets.
You need to recreate the Power View sheets as reports in the Power BI service.
Solution: From the Power BI service, get the data from SharePoint Online, and then click Import.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/power-bi/service-excel-workbook-files

NEW QUESTION: 2
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?
A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application.
B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate
preferences.
D. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
Answer: B

NEW QUESTION: 3
In determining the effectiveness of an entity's internal controls relating to the occurrence assertion for
payroll transactions, an auditor most likely would inquire about and:
A. Recompute the payroll deductions for employee fringe benefits.
B. Inspect evidence of accounting for prenumbered payroll checks.
C. Verify the preparation of the monthly payroll account bank reconciliation.
D. Observe the segregation of duties concerning personnel responsibilities and payroll disbursement.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Choice "A" is correct. The occurrence assertion as it relates to payroll transactions would correspond to an
audit objective to determine that payroll transactions actually occurred (i.e., that all payroll checks were
issued to valid employees for hours actually worked). Segregation of duties between personnel and payroll
departments is an important control to ensure that only valid employees receive paychecks.
Choice "B" is incorrect. Inspecting evidence related to prenumbering of payroll checks would relate to the
completeness assertion.
Choice "C" is incorrect. Recomputing payroll deductions is a substantive test related to the accuracy
assertion.
Choice "D" is incorrect. Verifying the preparation of the monthly payroll account bank reconciliation would
provide significant evidence for the accuracy assertion for payroll transactions.