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【Hardware】 Analytics-Con-301 Reliable Dumps Free & Analytics-Con-301 Exam Vce Free

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Salesforce Certified Tableau Consultant Sample Questions (Q23-Q28):NEW QUESTION # 23
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?
  • A. Use an extract filter.
  • B. Aggregate the extract to the visible dimensions.
  • C. Set up the extract as an incremental refresh.
  • D. Define the filters by using custom SQL.
Answer: A
Explanation:
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References: The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts. These practices suggest keeping the extract's data set short through filtering1. Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
Extract Filter: This involves setting filters that apply directly when the data is extracted from the source.
This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.

NEW QUESTION # 24
A database contains two related tables at different levels of granularity. The client wants to make all data available in Tableau Prep at the original level of granularity.
Which two solutions in Tableau meet the client's requirements? Choose two.
  • A. A single Published Data Source with a physical join between the two tables
  • B. Two separate Published Data Sources, one for each table
  • C. A Virtual Connection to the database and both tables within it
  • D. A single Published Data Source with a Relationship between the two tables
Answer: B,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The key requirement is:
# Data must remain at the original grain in Tableau Prep
# Tables are at different granularities
Tableau Prep does NOT support relationships, and automatically joins tables, which changes granularity (by duplicating or aggregating records).
Therefore, relationships (Option B) cannot preserve grain for Prep.
Also:
* A physical join (Option D) changes the grain by combining rows, often multiplying results when grain differs.
Only two options preserve the original granularity:
Option A - Two Separate Published Data Sources
Each data source represents one table.
In Tableau Prep, the user can choose:
* Use tables separately
* Join or clean them intentionally
* Keep each table at its own grain
This keeps all data at its native level.
Option C - Virtual Connection
A Virtual Connection:
* Publishes entire tables from the database
* Maintains each table independently at its native granularity
* Makes all tables available to Tableau Prep without altering grain
* Is specifically designed for governed, reusable multi-table access
Thus, it satisfies the requirement exactly.
Why the others are incorrect:
B - Relationship
Relationships only exist in Tableau Desktop logical layer, NOT in Tableau Prep.
Prep flattens the data # grain is lost.
D - Physical join
Always modifies granularity when tables differ, often causing row multiplication.
* Tableau Prep does not support logical relationships; only physical joins.
* Virtual Connections preserve original tables and governance.
* Published Data Sources can be separated to maintain original grain.

NEW QUESTION # 25
A client wants to grant a user access to a data source hosted on Tableau Server so that the user can create new content in Tableau Desktop. However, the user should be restricted to seeing only a subset of approved data.
How should the client set up the filter before publishing the hyper file so that the Desktop user follows the same row-level security (RLS) as viewers of the end content?
  • A. Extract Filter
  • B. Apply Filter to All Using Related Data Sources
  • C. Data Source Filter
  • D. Context Filter
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's row-level security (RLS) is applied at the data source level so that all users who connect to the data source-whether through Tableau Desktop, Server, or Cloud-see only the data they are permitted to see.
According to Tableau documentation:
* A Data Source Filter is the correct method for enforcing consistent row-level security for all users.
* When a Data Source Filter is applied before publishing, it becomes part of the data source's metadata and is applied every time any user connects to the published source.
* This ensures that users creating new workbooks in Tableau Desktop are governed by the same RLS as users viewing published dashboards.
Context filters and extract filters do not provide secure RLS:
* A Context Filter only applies inside the workbook where it is created. It does not enforce security in Tableau Desktop when the data source is reused.
* An Extract Filter physically removes rows from the extract but does not enforce role-based filtering or dynamic RLS.
* "Apply Filter to All Using Related Data Sources" affects workbook behavior, not published data source security.
A Data Source Filter applied prior to publishing is Tableau's documented approach for secure, reusable row- level security.
* Row-Level Security implementation guidance describing Data Source Filters as the foundation of secure RLS.
* Tableau Server publishing workflow indicating that Data Source Filters travel with the published source.
* Documentation on why Context and Extract Filters do not enforce user-dependent row-level security.

NEW QUESTION # 26
A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the following levels of data access that they can be granted.
. Group 1: Viewers who cannot see any information on profitability
. Group 2: Viewers who can see profit and profit margin
. Group 3: Viewers who can see profit margin but not the value of profit Which approach should the consultant use to provide the required level of access?
  • A. Use user filters to allow only Groups 2 and 3 access to data on profitability. Then, create a calculated field that limits visibility of profit value to Group 2 and use the calculation in the view in the report.
  • B. Specify with user filters in each view individuals who can see profit, profit margin, or none of these.
  • C. Specify in the row-level security (RLS) entitlement table individuals who can see profit, profit margin, or none of these. Then, use the table data to create user filters in the report.
  • D. Use user filters to access data on profitability to all groups. Then, create a calculated field that allows visibility of profit value to Group 2 and use the calculation in the view in the report.
Answer: A
Explanation:
The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.
References: The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation12. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.

NEW QUESTION # 27
A client wants to report Saturday and Sunday regardless of the workbook's data source's locale settings.
Which calculation should the consultant recommend?
  • A. DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7
  • B. DATEPART('weekday', [Order Date])>=6
  • C. DATENAME('iso-weekday', [Order Date])>=6
  • D. DATEPART('iso-weekday', [Order Date])>=6
Answer: A
Explanation:
The calculation DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7 is recommended because the ISO standard considers Monday as the first day of the week (1) and Sunday as the last day (7). This calculation will correctly identify Saturdays and Sundays regardless of the locale settings of the workbook's data source, ensuring that the report includes these days as specified by the client.
References: The use of the 'iso-weekday' part in the DATEPART function is consistent with the ISO 8601 standard, which is independent of locale settings. This approach is supported by Tableau's documentation on date functions and their behavior with different locale settings123.
To accurately identify weekends across different locale settings, using the 'iso-weekday' component is reliable as it is consistent across various locales:
ISO Weekday Function: The ISO standard treats Monday as the first day of the week (1), which makes Sunday the seventh day (7). This standardization helps avoid discrepancies in weekday calculations that might arise due to locale-specific settings.
Identifying Weekends: The calculation checks if the 'iso-weekday' part of the date is either 1 (Sunday) or 7 (Saturday), thereby correctly identifying weekends regardless of the locale settings.
References:
Handling Locale-Specific Settings: Using ISO standards in date functions allows for uniform results across systems with differing locale settings, essential for consistent reporting in global applications.

NEW QUESTION # 28
......
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