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Latest Analytics-Con-301 Exam Dumps Questions
The dumps for Analytics-Con-301 exam was last updated on Dec 27,2025 .
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A client is concerned that a dashboard has experienced degraded performance after they added additional quick filters. The client asks a consultant to improve performance. Which two actions should the consultant take to fulfill the client's request? Choose two.
Explanation: Comprehensive and Detailed Explanation From Exact Extract: Quick filters are one of the most expensive features in Tableau because they require queries to populate value lists and dynamic recalculations when filters change. According to Tableau performance documentation:
A client is working in Tableau Prep and has a field named Orderld that is compiled by country, year, and an order number as shown in the following table. What should the consultant use to transform the table in the most efficient manner?
Explanation: To transform the Orderld field in Tableau Prep, the Split option is the most efficient and straightforward method. Here’s how you can apply it: In Tableau Prep, drag your dataset into the flow. Click on the Orderld field in the workspace to select it. Look for the option in the toolbar that says "Split" and select it. Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split the Orderld into multiple new fields. The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.). The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the original Orderld into the desired components without manual specification of conditions or writing any formulas. Reference This procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.
A client wants guidance for Creators to build efficient extracts from large data sources. What are three Tableau best practices that the Creators should use? Choose three.
Explanation: To build efficient extracts from large data sources, it is crucial to minimize the load and optimize the performance of the extracts: A. Keep only the data required for analysis by using extract filters: This best practice involves using filters to reduce the volume of data extracted, thus focusing only on the data necessary for analysis. B. Use aggregate data for visible dimensions, whenever possible: Aggregating data at the time of extraction reduces the granularity of the data, which can significantly improve performance and reduce the size of the extract. E. Hide all unused fields: Removing fields that are not needed for analysis from the extract reduces the complexity and size of the data model, which in turn enhances performance and speeds up load times. These practices are endorsed in Tableau’s official documentation and training sessions as effective ways to enhance the performance of Tableau extracts and optimize dashboard responsiveness.
A Tableau consultant tasked with evaluating a data structure is handed the below sample dataset. Which two statements are true about the dataset? Choose two.
Explanation: The dataset shown is a classic “wide” format”: A single row per state Separate columns for each year: 2019, 2020, 2021, 2022, 2023, 2024 Tableau’s documentation on data structure and pivoting explains: ✔ Why A is TRUE Tableau documentation identifies wide datasets (multiple columns representing categories such as years, months, or similar time periods) as high-maintenance structures because: For every new year, a new column must be added. Metadata and calculations must be updated each time. This type of structure is described as having poor scalability and higher maintenance. This dataset fits that exact description, so A is correct. ✔ Why C is TRUE According to Tableau’s “Pivot Data from Columns to Rows” section: Wide datasets can and should often be pivoted so that repeated columns (such as year columns) become rows. Pivoting enables dynamic capabilities such as: Year filters (year selector) Time-series analysis Consistent aggregations Simplified calculations Pivoting this dataset would produce: State Year Value Alabama 2019
A company uses an extract built from Custom SQL joining Claims and Members. Members have multiple records in both tables → causing data duplication, which results in inflated claim cost trends. Which approach meets performance and maintenance goals?
Explanation: Comprehensive and Detailed Explanation From Exact Extract: The problem: Custom SQL joins two multi-row tables, causing→ many-to-many duplication. This artificially multiplies claim costs. The extract becomes heavy and slow due to Custom SQL. Tableau’s recommended solution: ✔ Use Relationships in the Logical Layer ✔ Instead of physical joins ✔ Tableau resolves many-to-many issues automatically ✔ Query is generated at the appropriate granularity to avoid duplication This is exactly Option A. Relationships allow the Claims facts to remain at the claim grain and Members to remain at the member grain. Tableau resolves aggregations correctly, preventing inflated values. Why the others are incorrect: B ― Physical Join Would continue the same duplication problem because multi-row joins multiply rows. C ― LODs Would require complex calculations and are error-prone. They do NOT fix the duplication in the underlying extract. D ― Table Calculations Happen after Tableau aggregates the duplicated data ― too late to fix the inflated baseline numbers. Thus, the only correct and modern solution is relationships. Relationships documentation explaining resolution of many-to-many granularity issues. Guidance recommending avoiding Custom SQL for performance reasons. Logical Layer behavior preventing row-duplication errors.
Exam Code: Analytics-Con-301 Q & A: 100 Q&As Updated: Dec 27,2025
