Analytics-Con-301 Test Simulator Online, Reliable Analytics-Con-301 Exam Simulator

P.S. Free & New Analytics-Con-301 dumps are available on Google Drive shared by Test4Engine: https://drive.google.com/open?id=14IPVVK8U2_2-ItqanXuw02j1a992xz6F

With a high quality, we can guarantee that our Analytics-Con-301 practice quiz will be your best choice. There are three different versions about our products, including the PDF version, the software version and the online version. The three versions are all good with same questions and answers; you can try to use the version of our Analytics-Con-301 Guide materials that is suitable for you. Our Analytics-Con-301 exam questions have many advantages, I am going to introduce you the main advantages of our Analytics-Con-301 study materials, I believe it will be very beneficial for you and you will not regret to use our Analytics-Con-301 learning guide.

Prepared by experts and approved by experienced professionals, our Analytics-Con-301 exam torrent is well-designed high quality products and they are revised and updated based on changes in syllabus and the latest developments in theory and practice. With the guidance of our Analytics-Con-301 Guide Torrent, you can make progress by a variety of self-learning and self-assessing features to test learning outcomes. And as the high pass rate of our Analytics-Con-301 exam questions is 99% to 100%, you will be bound to pass the Analytics-Con-301 exam with ease.

>> Analytics-Con-301 Test Simulator Online <<

Reliable Analytics-Con-301 Exam Simulator, Test Analytics-Con-301 Registration

Test4Engine are stable and reliable exam questions provider for person who need them for their exam. We have been staying and growing in the market for a long time, and we will be here all the time, because the excellent quality and high pass rate of our Analytics-Con-301 Exam Questions. As for the safe environment and effective product, there are thousands of candidates are willing to choose our Analytics-Con-301 study question, why don’t you have a try for our study question, never let you down!

Salesforce Analytics-Con-301 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Analysis: This domain targets Tableau Consultants to plan and prepare data connections effectively. It includes recommending data transformation strategies, designing row-level security (RLS) data structures, and implementing advanced data connections such as Web Data Connectors and Tableau Bridge. Skills in specifying granularity and aggregation strategies for data sources across Tableau products are emphasized.
Topic 2	IT Management: This domain measures skills related to managing Tableau environments. It includes planning server upgrades, recommending deployment solutions (on-premise or cloud), and ensuring alignment between technical and business requirements for analytics infrastructure. It also involves troubleshooting and optimizing system performance relevant to Tableau Server and Cloud deployments.
Topic 3	Business Consulting: For Tableau Consultants, this section involves designing and troubleshooting calculations and workbooks to meet advanced analytical use cases. It covers selecting appropriate chart types, applying Tableau’s order of operations in calculations, building interactivity into dashboards, and optimizing workbook performance by resolving resource-intensive queries and other design-related issues.

Salesforce Certified Tableau Consultant Sample Questions (Q53-Q58):

NEW QUESTION # 53
A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:
Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}
The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher than the customer expects.
The client asks a consultant to rewrite the calculation so the result meets their expectation.
Which calculation should the consultant use?

A. {INCLUDE [Customer ID]: COUNTD([Order ID])}
B. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
C. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}
D. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}

Answer: D

Explanation:
The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.
References: The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis. The use of {FIXED} expressions to specify the granularity of the calculation is a common practice and is well-documented in Tableau's official resources12.
The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:
FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues.
Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.
References:
Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.

NEW QUESTION # 54
A client collects information about a web browser customers use to access their website. They then visualize the breakdown of web traffic by browser version.
The data is stored in the format shown below in the related table, with a NULL BrowserID stored in the Site Visitor Table if an unknown browser version accesses their website.

The client uses "Some Records Match" for the Referential Integrity setting because a match is not guaranteed. The client wants to improve the performance of the dashboard while also getting an accurate count of site visitors.
Which modifications to the data tables and join should the consultant recommend?

A. Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."
B. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."
C. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and change the Referential Integrity to "All Records Match."
D. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and leave the Referential Integrity set to
"Some Records Match."

Answer: C

Explanation:
To improve the performance of a Tableau dashboard while maintaining accurate counts, particularly when dealing with unknown or NULL BrowserIDs in the data tables, the following steps are recommended:
Modify the Browser Table: Add a new row to the Browser Table labeled "Unknown," assigning it a unique BrowserID, e.g., 0 or 4.
Update the Site Visitor Table: Replace all NULL BrowserID entries with the BrowserID assigned to the "Unknown" entry. This ensures every record in the Site Visitor Table has a valid BrowserID that corresponds to an entry in the Browser Table.
Change Referential Integrity Setting: Change the Referential Integrity setting from "Some Records Match" to "All Records Match." This change assumes all records in the primary table have corresponding records in the secondary table, which improves query performance by allowing Tableau to make optimizations based on this assumption.
References:
Handling NULL Values: Replacing NULL values with a valid unknown option ensures that all data is included in the analysis, and integrity between tables is maintained, thereby optimizing the performance and accuracy of the dashboard.

NEW QUESTION # 55
A stakeholder has multiple files saved (CSV/Tables) in a single location. A few files from the location are required for analysis. Data transformation (calculations) is required for the files before designing the visuals. The files have the following attributes:
. All files have the same schema.
. Multiple files have something in common among their file names.
. Each file has a unique key column.
Which data transformation strategy should the consultant use to deliver the best optimized result?

A. Apply the data transformation (calculations) in each require file and do the join to combine/merge before designing the visuals.
B. Use join option to combine/merge all the files together before doing the data transformation (calculations).
C. Apply the data transformation (calculations) in each require file and do the wildcard union to combine/merge before designing the visuals.
D. Use wildcard Union option to combine/merge all the files together before doing the data transformation (calculations).

Answer: D

Explanation:
Moving calculations to the data layer and materializing them in the extract can significantly improve the performance of reports in Tableau. The calculation ZN([Sales])*(1 - ZN([Discount])) is a basic calculation that can be easily computed in advance and stored in the extract, speeding up future queries. This type of calculation is less complex than table calculations or LOD expressions, which are better suited for dynamic analysis and may not benefit as much from materialization12.
References: The answer is based on the best practices for creating efficient calculations in Tableau, as described in Tableau's official documentation, which suggests using basic and aggregate calculations to improve performance1. Additionally, the process of materializing calculations in extracts is detailed in Tableau's resources2.
Given that all files share the same schema and have a common element in their file names, the wildcard union is an optimal approach to combine these files before performing any transformations. This strategy offers the following advantages:
Efficient Data Combination: Wildcard union allows multiple files with a common naming scheme to be combined into a single dataset in Tableau, streamlining the data preparation process.
Uniform Schema Handling: Since all files share the same schema, wildcard union ensures that the combined dataset maintains consistency in data structure, making further data manipulation more straightforward.
Pre-Transformation Combination: Combining the files before applying transformations is generally more efficient as it reduces redundancy in transformation logic across multiple files. This means transformations are written and processed once on the unified dataset, rather than repeatedly for each individual file.
References:
Wildcard Union in Tableau: This feature simplifies the process of combining multiple similar files into a single Tableau data source, ensuring a seamless and efficient approach to data integration and preparation.

NEW QUESTION # 56
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?

A. [Sales]
>
{ FIXED [Order ID] : SUM([Sales]) }
B. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : SUM([Sales]) }
C. { FIXED [Order ID] : SUM([Sales]) }
>
{ INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }
D. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

Answer: D

Explanation:
To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:
Correct Formula: { FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } This calculation uses a Level of Detail (LOD) expression:
The left part of the formula { FIXED [Order ID] : SUM([Sales]) } calculates the total sales for each individual order.
The right part { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } calculates the average sales per order within each region.
The > operator is used to compare these two values to determine if the sales for each order exceed the regional average.
References
This formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.

NEW QUESTION # 57
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('weekday', [Order Date])>=6
B. DATEPART('iso-weekday', [Order Date])>=6
C. DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7
D. DATENAME('iso-weekday', [Order Date])>=6

Answer: C

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 # 58
......

You don't need to install any separate software or plugin to use it on your system to practice for your actual Salesforce Certified Tableau Consultant (Analytics-Con-301) exam. Test4Engine Salesforce Analytics-Con-301 web-based practice software is supported by all well-known browsers like Chrome, Firefox, Opera, Internet Explorer, etc.

Reliable Analytics-Con-301 Exam Simulator: https://www.test4engine.com/Analytics-Con-301_exam-latest-braindumps.html

DOWNLOAD the newest Test4Engine Analytics-Con-301 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=14IPVVK8U2_2-ItqanXuw02j1a992xz6F

Our Blog

Jon Fox Jon Fox

Biography

Analytics-Con-301 Test Simulator Online, Reliable Analytics-Con-301 Exam Simulator

Reliable Analytics-Con-301 Exam Simulator, Test Analytics-Con-301 Registration

Salesforce Analytics-Con-301 Exam Syllabus Topics:

Salesforce Certified Tableau Consultant Sample Questions (Q53-Q58):

Hello world!

Learn Webs Applications Development from Experts