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Salesforce Analytics-DA-201 Exam Overview:
| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Tableau Data Analyst Exam |
| Exam Number: | Analytics-DA-201 |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Available Languages: | English |
| Related Certifications: | Tableau Certified Data Analyst (legacy naming variants) Tableau Desktop Specialist |
| Certificate Validity Period: | Maintenance required annually via Salesforce certification maintenance program |
| Exam Format: | Proctored online or onsite, Multiple choice, Multiple select |
| Exam Duration: | 105 minutes |
| Real Exam Qty: | Approximately 30–60 (varies, not officially fixed) |
| Exam Price: | $200 USD (may vary by region) |
| Recommended Training: | Tableau Data Analyst Learning Path (Trailhead) Tableau Training and Tutorials |
| Exam Registration: | Salesforce Certification Registration Kryterion Webassessor Exam Scheduling |
| Sample Questions: | Salesforce Analytics-DA-201 Sample Questions |
| Exam Way: | Online proctored exam or onsite testing center |
| Pre Condition: | No formal prerequisites required, but experience with Tableau and data analysis is strongly recommended |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/tableau-data-analyst |
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Salesforce Analytics-DA-201 Exam Syllabus Topics:
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Salesforce Certified Tableau Data Analyst Sample Questions (Q143-Q148):
NEW QUESTION # 143
A Data Analyst has a sales dataset that contains the following fields.
The analyst needs to analyze the average revenue per item sold in different regions over time.
Which two fields should the analyst measure? Choose two.
- A. Order Date
- B. Customer Region
- C. Quantity
- D. Revenue
- E. Product Name
Answer: C,D
Explanation:
Revenue and Quantity are the two quantitative fields that must be measured to calculate average revenue per item. The analyst can derive the required business metric by relating the monetary Revenue measure to the Quantity measure, such as revenue divided by units sold at the appropriate aggregation level. Customer Region is required to segment the analysis geographically, but it is a dimension rather than the numeric value being measured. Order Date provides the time dimension for analysing the metric over time, and Product Name can further categorise the data, but neither is the primary quantitative measure required by the question.
Tableau defines measures as quantitative fields that can be numerically aggregated, while dimensions are qualitative fields used to group or segment the data. Therefore, Revenue and Quantity are the appropriate selections.
NEW QUESTION # 144
You are developing a data source in Tableau Prep.
You have two tables named Orders and Employees.
The Orders table was created in 2019 and contains the following columns.
The Employees table contains all the employee information for the various sales teams in the sales department and contains the following columns
You want to prepare the data so that you can analyze sales trends over time for every sales team.
Which three actions should you perform in order? (Place the three correct options in order. Use the arrows to move Options to Answer Area. Use Answer Area arrows to re-order the options.)
Answer:
Explanation:
Explanation:
There are different ways to prepare the data in Tableau Prep, but one possible answer is:
Answer area:
* Join the tables.
* Aggregate the data by Sales Team.
* Add the following filter to the Employees table: Sale Team - Team Alpha.
To analyze sales trends over time for every sales team, you need to combine the data from the Orders and Employees tables. You can do this by joining the tables on the Rep ID column, which is a common identifier for both tables. This will create a single table with all the columns from both tables. Next, you need to aggregate the data by Sales Team, which is the level of analysis you want. You can do this by grouping the data by the Sales Team column and applying an aggregation function (such as sum, average, count, etc.) to the other columns that you want to measure (such as Total Amount, Rep Tenure, etc.). This will create a summary table with one row for each sales team and the aggregated values for the other columns. Finally, you need to filter the data to focus on a specific sales team, such as Team Alpha. You can do this by adding a filter to the Employees table and selecting only the rows where Sales Team equals Team Alpha. This will remove the rows for other sales teams and keep only the data for Team Alpha.
References:
[Tableau Certified Data Analyst Exam Prep Guide], page 9, section "Preparing Data"
[Tableau Help: Join Your Data]
[Tableau Help: Aggregate Your Data]
[Tableau Help: Filter Your Data]
NEW QUESTION # 145
You have a sales dataset that contains the following fields.
You need to analyze the average revenue per product in different regions over time.
Which two fields should be measures? Choose two.
- A. Order Date
- B. Customer Region
- C. Quantity
- D. Revenue
- E. Product Name
Answer: C,D
Explanation:
To analyze the average revenue per product in different regions over time, you need to use two fields that contain numeric, quantitative values that you can measure and aggregate. Quantity and Revenue are both measures that fit this criterion. You can multiply Quantity and Revenue to get the total sales for each product, and then divide by the number of products to get the average revenue. You can also use these measures to create charts and tables that show the trends and comparisons over time and across regions. References:
* Dimensions and Measures, Blue and Green - Tableau
* Tableau Certified Data Analyst Study Guide
NEW QUESTION # 146
You have a dataset that contains people and the awards they have won.
Which formula should you use to get the number of different types of awards that have been won?
- A. COUNTD {(NAME])
- B. COVAR({Award})
- C. INDEX()
- D. MAX {(Award})
- E. COUNTD({Award})
Answer: E
Explanation:
To get the number of different types of awards that have been won, you should use the formula COUNTD ({Award}). This formula will return the count of distinct values in the Award field, which are the different types of awards that have been won.
The other options are not correct for this scenario. COVAR({Award}) is not a valid function in Tableau. MAX ({Award}) will return the maximum value in the Award field, which may not be a type of award. INDEX() will return the index or rank of each row in a partition, which is not related to the types of awards. References:
https://help.tableau.com/current/pro/desktop/en-us/functions_functions_aggregate.htm#COUNTD https://help.
tableau.com/current/pro/desktop/en-us/functions_functions_tablecalculation.htm#INDEX The COUNTD function in Tableau calculates the number of distinct (unique) items in a field. When looking to get the number of different types of awards that have been won, the COUNTD([Award]) function will count each unique award name only once, regardless of how many times it appears in the dataset. This will return the total number of unique award types.
NEW QUESTION # 147
You have the following map.
You need the map to appear as shown in the following visualization.
What should you do?
- A. Change the mark type to Map
- B. Change the mark type to Density
- C. Drag Location to Size on the Marks card
- D. Drag Population to Size on the Marks card
- E. Change the opacity to 75%
Answer: B
Explanation:
The question presents a scenario where a geographic map visualization in Tableau needs to be transformed from a series of discrete circles representing data points to a density map visualization. The density map shows concentrations of data points with a gradient of color, where denser areas are indicated by a darker color.
Here's the explanation for each option:
A). Change the mark type to Density: This is the correct answer because changing the mark type to 'Density' in Tableau creates a density map, which displays the concentration of data points with a color gradient. This is exactly what is needed to achieve the visual effect shown in the second image, where regions with a higher concentration of data points are represented by darker shades.
B). Drag Location to Size on the Marks card: This option would adjust the size of the marks based on the number of locations, which is not relevant to creating a density map. It would result in varying sizes of circles, not a continuous gradient.
C). Change the mark type to Map: The visualization is already using a map. This option would not change the visualization to the desired density map.
D). Drag Population to Size on the Marks card: This would change the size of the circles based on the population values, making some circles larger and others smaller. This is not how a density map is created, which uses color intensity rather than size to show concentration.
E). Change the opacity to 75%: Changing the opacity would affect the transparency of the marks on the map but would not transform the visualization into a density map.
To achieve the visualization shown in the second image, the mark type must be changed to 'Density,' which will produce a heat map-like effect where the color intensity represents the concentration of data points.
Therefore, the correct answer is A. Change the mark type to Density.
To create a density map from a scatter plot of data points, you would change the mark type to Density. This mark type allows you to visualize the concentration of data points in an area, which can be useful for identifying clusters or patterns in geospatial data.
NEW QUESTION # 148
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