Exam: Analytics-101 Dumps

The Analytics-Con-202 Salesforce Certified Tableau Next Consultant Exam is designed for professionals who implement and maintain analytics solutions within the Salesforce ecosystem. The certification focuses on Tableau Next, Data 360, semantic models, Agentic Analytics, dashboards, cross-cloud integration, administration, and workspace management.

Salesforce describes the target candidate as someone with experience across Salesforce, Data 360/Data Cloud architecture, analytics, semantic data models, and Tableau Next implementations.

Analytics-Con-202 Exam Overview

The Salesforce Certified Tableau Next Consultant exam measures practical knowledge required to transform enterprise data into actionable analytics and insights.

Exam Code: Analytics-Con-202
Exam Name: Salesforce Certified Tableau Next Consultant
Vendor: Salesforce
Questions: 60 scored questions + up to 5 unscored questions
Duration: 105 minutes
Passing Score: 65%
Prerequisite: None
Exam Delivery: Online proctored or testing center

Analytics-Con-202 Exam Topics

1. Data Setup — 20%
Candidates should understand:
Semantic model components, design, and application
Semantic data models
Data 360 capabilities
Data integration and data management
Data preparation and modeling
Data architecture
Data processing approaches
Selecting an appropriate data architecture for a business scenario
Preparing data for analytics and Agentic experiences

Data Setup provides the foundation for Tableau Next analytics because semantic models and properly prepared data determine how information can be analyzed and interpreted.

2. Agentic Experiences — 25%
This is the largest Analytics-Con-202 exam domain.
Important areas include:
Generative AI capabilities in Tableau Next
Agentic readiness
Analytics Agent
Exploring data with Analytics Agent
Generating data insights
Proactive data alerts
Notifications
Preparing analytics environments for AI-assisted experiences
Using trusted and well-defined analytical data

Candidates should understand how AI-powered analytics interacts with semantic models and business data.

3. Embedding, Cross-Cloud, - Interoperability — 20%
Key topics include:
Tableau Next integration with Salesforce analytics platforms
Embedding Tableau Next assets
Cross-platform workflows
Tableau Next Apps for Salesforce
Marketplace offerings
Surfacing analytics within user workflows
Developer tools
APIs
Salesforce ecosystem interoperability

This section tests how Tableau Next can operate as part of a broader Salesforce environment rather than as an isolated analytics platform.

4. Basic Setup - Admin — 10%
Study:
Activating Tableau Next features
Managing Tableau Next
User access
Permissions
Appropriate access configuration
Agentic Analytics activation
Administrative configuration

5. Visualizations - Dashboards — 15%
Important areas include:
Tableau Next visualization best practices
Dashboard design
User-friendly analytical experiences
Dashboard actions
Interactive analytics
Designing visualizations for business users
Configuring actions within Tableau Next

6. Managing Workspaces - Orgs — 10%
Candidates should understand:
Personal Org deployment and management
Tableau Next workspaces
Asset management
Asset sharing
Workspace permissions
Tableau Next asset deployment
Managing analytics content across environments

The six domains and their published weightings are reflected in the current Salesforce exam guide.

CertKingdom Analytics-Con-202 preparation materials help Salesforce professionals study for the Salesforce Certified Tableau Next Consultant exam with practice questions, study resources, and test preparation covering Data Setup, Agentic Experiences, Tableau Next integration, administration, dashboards, and workspace management.

For SEO, it is safer to position CertKingdom as a practice-test and exam-preparation provider rather than claiming that unauthorized - real exam dumps - are guaranteed or identical to Salesforce's live exam.

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The Salesforce Certified Tableau Desktop Foundations exam validates your ability to build, analyze, and share data
visualizations using Tableau Desktop. This certification demonstrates foundational competency across the entire
workflow: from ingesting and structuring data through preparing analytical views and publishing insights to stakeholders.

Domain 1: Connecting to and Preparing Data (23% Weight)
This domain covers the essential groundwork for any Tableau project. You must understand how to establish
connections to various data sources and determine whether a live connection or periodic data extraction best suits your
analytical needs. Central to this domain is the ability to structure your data appropriately through relationships and
joins, then refine individual data fields by adjusting their properties, types, and behaviors.

* Data Connection Strategies: Establish real-time connections to databases and external sources; create and manage
periodic data extracts for improved performance; preserve connection metadata and settings for reusability; integrate
multiple data sources within a single analytical model.
* Data Structure and Relationships: Define how tables connect through relationships and joins; make informed
decisions about relationship versus join implementation based on your analytical requirements; combine data through
union operations when needed.

* Field Configuration: Rename and alias fields for clarity; assign geographic properties to location data; convert field
data types to match analytical needs; customize default behaviors such as aggregation methods, number formatting,
and date display.

Domain 2: Exploring and Analyzing Data (37% Weight)
This is the largest domain and represents the core analytical work in Tableau. You will create a variety of visualization
types appropriate for different data stories, organize your data through groups, sets, and hierarchies, and apply
analytical techniques that reveal patterns and trends. Competency here means moving beyond basic charts to employ
sorting, reference lines, calculations, and statistical summaries that drive insights.
* Visualization Creation: Build standard chart types including bar, line, and scatter plots; create geographic
visualizations for location-based analysis; construct advanced layouts such as combined axis and dual axis charts;
layer visualizations for stacked representations; generate statistical visualizations like density maps and histograms;
design tabular views with crosstabs and conditional highlighting.
* Data Organization and Filtering: Create logical groupings of dimension members using multiple methods; define
dynamic sets to isolate specific data subsets; establish hierarchical structures within your dimensions; apply filters at
various levels; implement time-based filters for temporal analysis.
* Analytical Techniques: Apply manual and automatic sorting to emphasize rankings; overlay reference lines for
benchmarking; leverage quick table calculations for ratio and year-over-year analysis; construct bins and histograms
for distribution analysis; build calculated fields combining string, date, and numeric operations; employ parameters to
create dynamic, user-driven views; add totals and subtotals for aggregated summaries.

Domain 3: Sharing Insights (25% Weight)
After building analytical views, you must present them effectively and enable other users to access and interact with your work.
This domain encompasses the visual design choices that make dashboards compelling and the publishing
strategies that put insights into the hands of decision-makers.
* Visual Presentation: Apply color palettes and conditional coloring to encode data relationships; select and configure
typography for readability; represent data using custom shape assignments; implement animation effects to guide
viewer attention; adjust mark sizes to emphasize importance; manage legend visibility and placement.
* Dashboard Development: Assemble multiple worksheets into cohesive dashboards; embed interactive controls such
as filter buttons, data highlighters, and dynamic tooltips; establish dashboard actions that link user interactions across
multiple views; arrange dashboard layouts responsively for different device sizes; construct multi-step narratives through story features.
* Distribution and Export: Publish workbooks to Tableau Server for organizational sharing; export visualizations in
multiple formats including PDF and images; generate PowerPoint presentations directly from Tableau; provide access
to underlying data for verification and deeper exploration.
Domain 4: Understanding Tableau Concepts (15% Weight)
This domain establishes the conceptual foundation underlying Tableau's approach to data analysis. A firm grasp of these
concepts informs all decisions made across the other domains, from how you structure data to how Tableau renders your
visualizations.
* Dimensions versus Measures: Recognize that dimensions typically contain categorical, descriptive information used
for grouping and filtering, while measures contain numeric values designed for aggregation and calculation;
understand how this distinction influences chart design and analytical capability.
* Discrete and Continuous Fields: Differentiate how Tableau displays discrete fields as individual categories or
headers versus continuous fields as axes with interpolated values; recognize that date fields can be treated as discrete
parts or as continuous sequences, each producing different visual outcomes.
* Aggregation Behavior: Understand that measures automatically aggregate using a default function determined by
Tableau; recognize that adding dimensions to a view changes how measures aggregate, breaking totals into
component groups and revealing patterns invisible in aggregated-only views.

QUESTION 1
You are building a Tableau workbook to analyze sales data that includes customer information from
Salesforce and transaction details from a data warehouse. The customer and transaction data are stored in
separate systems and must be combined in your data source. You want to ensure that Tableau retrieves the
data in real time to reflect the latest customer updates and transaction records.
Which approach should you use to create your data source?

A. Create a live connection to Salesforce and an extract from the data warehouse, then add a relationship between them
B. Create a live connection to both Salesforce and the data warehouse, then add a relationship between them
C. Create extracts from both Salesforce and the data warehouse, then add a join between them in the data model
D. Create a live connection to the data warehouse and use a manual union to combine it with exported CSV files from Salesforce
E. Create a .TDS file that stores both data sources as live connections without defining any relationships

Answer: B

Explanation:
The correct answer is Create a live connection to both Salesforce and the data warehouse, then add a
relationship between them.
Since the requirement is to retrieve data in real time from both systems, both connections must be live
connections-not extracts. Extracts create a snapshot of data at a point in time and would not reflect real-time
updates. Relationships in Tableau are the appropriate way to connect multiple tables from different sources in
a modern data model; they are more flexible than joins and handle many-to-many relationships more effectively.
Why the other options are incorrect:
* Option 1 mixes live and extract connections, violating the real-time requirement for the Salesforce data.
* Option 3 uses extracts, which would not provide real-time updates.
* Option 4 uses CSV files and manual unions, which do not provide a live connection to either system.
* Option 5 omits the crucial relationship that connects the two data sources together.

QUESTION 2
You are creating a dashboard to track regional sales performance. Your source data contains sales amounts
by month and region. Currently, when you add Region to the Rows shelf and Month to the Columns shelf, you
see individual rows for each region and individual columns for each month. You need to create a visualization
where users can quickly see total sales for selected regions without having to navigate through multiple rows.
Which of the following actions would best support your business requirement?

A. Create a set from the Region dimension, then add it to the Filters shelf to allow users to select which regions to display
B. Create a group from the Region dimension and then sort the groups alphabetically to make navigation easier
C. Create a hierarchy from Region and Month, then use the hierarchy drill-down to collapse unwanted regions
D. Add a reference line to the visualization at the mean sales value to highlight high-performing regions
E. Create a calculated field that sums sales by region, then replace Region on Rows with this new field

Answer: A

Explanation:
The correct answer is Create a set from the Region dimension, then add it to the Filters shelf to allow
users to select which regions to display.
A set is a custom subset of dimension values that can be toggled on and off dynamically, making it ideal for
allowing users to filter and focus on specific regions without seeing unwanted rows. When a set is placed on the
Filters shelf, it becomes an interactive control that users can use to quickly show only the regions they want to analyze.
Why the other options are incorrect:
* Option 2 creates a group, which is useful for combining values but does not provide a dynamic filter for users
to toggle specific regions on and off.
* Option 3 creates a hierarchy, which enables drill-down navigation but does not reduce the number of regions
displayed at once; it adds navigation complexity rather than solving the problem.
* Option 4 adds a reference line, which is a statistical annotation tool and does not help users filter or focus on
specific regions.
* Option 5 would replace the individual region rows with a single calculated aggregate, losing the ability to
compare regions side by side.

QUESTION 3
You are preparing a Tableau workbook to share with executives who need to analyze year-over-year revenue
trends. Your data includes a Date field containing transaction dates from 2023, 2024, and 2025. You have
created a line chart with Year on Columns and Revenue (aggregated as Sum) on Rows, producing three
separate line marks instead of a single continuous line across years.
Which of the following best explains what is happening and how to fix it?

A. Year is discrete (blue), which creates separate marks for each year value. Change Year to continuous (green) to create a single connected line across all years
B. The aggregation on Revenue is set to Sum, which breaks the continuity. Change it to Average to create a single continuous line
C. The Date field has not been assigned a geographic role, so Tableau cannot order the years correctly. Assign the Temporal geographic role to the Date field
D. You need to create a manual sort on the Year field to establish the correct order for the line to connect across years
E. The chart type should be changed from line to area to show year-over-year trends as a continuous series

Answer:
Explanation:
The correct answer is Year is discrete (blue), which creates separate marks for each year value.
Change Year to continuous (green) to create a single connected line across all years.
In Tableau, discrete (blue) fields create separate columns, rows, or marks for each distinct value, whereas
continuous (green) fields create a single axis with values plotted along a spectrum. When Year is treated as
discrete, Tableau displays separate line segments for each year value. Converting the Year field to continuous
transforms it into an axis with all years plotted along a continuum, allowing the line to connect continuously
across all three years.
Why the other options are incorrect:
* Option 2 misidentifies the root cause; the aggregation method (Sum vs. Average) does not determine
whether marks are connected into a continuous line.
* Option 3 confuses geographic roles (used for mapping) with temporal field configuration; geographic roles
do not affect line continuity.
* Option 4 addresses sort order, which affects the appearance of an already-connected line but does not solve
the fundamental issue of disconnected marks.
* Option 5 changes the chart type unnecessarily; an area chart would have the same problem because the
underlying Year field is still discrete.

QUESTION 4
You are building a Tableau workbook connected to a Salesforce database containing 2 million customer
records. Your stakeholders need to see real-time sales data that updates as transactions are entered in
Salesforce. However, your team also plans to perform complex calculations and filtering that may impact
performance during peak hours.
Which combination of connection type and data strategy would best address this requirement?

A. Use a live connection so that all data is queried in real time from Salesforce, and add a reference line
calculation to minimize performance impact
B. Create an extract of the Salesforce data and refresh it every 15 minutes during business hours to
balance real-time visibility with stable performance for calculations
C. Use a live connection for exploratory analysis, then transition all production dashboards to an extract
refreshed on a scheduled basis
D. Create a live connection and disable all filters to ensure data refreshes without lag
E. Use only extracts and manually trigger refreshes after each transaction to guarantee real-time data

Answer: B, C

Explanation:
The correct answers recognize the core trade-off between live connections and extracts. Live connections
query the source system in real time, ensuring the most current data but potentially causing performance
issues with large datasets and complex calculations. Extracts pre-aggregate and store data locally, enabling
fast performance and complex analytics but introducing a refresh latency.
Answer 2 is correct because scheduling extract refreshes every 15 minutes provides near-real-time visibility for
most business use cases while avoiding the performance bottlenecks of live connections on 2 million records.
Answer 3 is also correct because it acknowledges that both approaches can coexist: live connections work well
for ad-hoc exploration, while scheduled extracts are more appropriate for production dashboards with heavy calculations.
Answer 1 fails because live connections do not inherently prevent performance issues; a reference line
calculation does not address the underlying query load. Answer 4 incorrectly assumes that disabling filters
improves performance. Answer 5 is impractical because manual triggers do not scale to real-time requirements
and violate the real-time visibility requirement.

QUESTION 5
You are preparing a data source in Tableau Desktop that combines customer demographic data (Customer
ID, Name, Region) from a SQL Server table with their purchase history (Customer ID, Order Date, Amount)
from a separate Oracle database. You need to ensure that a single data source can analyze both dimensions
and measures from both systems without requiring manual data blending on each worksheet.
What should you do to enable this analysis?

A. Add two separate live connections to the data source, then create a join between the Customer ID
fields on the data source page
B. Create an extract from SQL Server and a separate extract from Oracle, then use data blending in the
worksheet to combine them
C. Create a single live connection to SQL Server and use a cross-database join to query Oracle tables directly
within the data source
D. Add both connections to a single data source and create a relationship using Customer ID as the linking field
E. Manually combine the data in Excel and then create a single extract in Tableau

Answer: A, D

Explanation:
This question tests understanding of multi-connection data sources and the difference between joins and relationships.
Answers 1 and 4 are correct because both represent valid approaches to creating a data source with multiple
connections. Answer 1 explicitly describes creating a join between two live connections, which unifies the data
at the source level. Answer 4 describes adding both connections and using a relationship, which is the modern
Tableau approach that provides flexibility for handling complex hierarchies and many-to-many relationships.
Answer 2 is incorrect because while data blending is a valid feature, it must be configured on individual
worksheets and does not create a unified data source. This approach would require repeated blending setup
across every worksheet. Answer 3 is incorrect because Tableau does not natively execute cross-database joins;
each connection represents a separate data system.
Answer 5 is impractical and negates the benefits of Tableau's data connectivity features. The correct
approaches leverage Tableau's ability to define the data model once and reuse it across all worksheets.

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FAQ Section

15 Frequently Asked Questions (FAQs)



1. What is Analytics-Con-202?

Analytics-Con-202 is the exam code for the Salesforce Certified Tableau Next Consultant certification.

2. What does the Salesforce Tableau Next Consultant certification cover?

The exam covers Data Setup, Agentic Experiences, Embedding/Cross-Cloud/Interoperability, Basic Setup & Admin, Visualizations & Dashboards, and Managing Workspaces & Orgs.

3. How many questions are on Analytics-Con-202?

The current Salesforce exam guide specifies 60 scored multiple-choice questions plus up to 5 unscored questions.

4. How long is the Analytics-Con-202 exam?

Candidates have 105 minutes to complete the exam.

5. What is the passing score for Analytics-Con-202?

The published passing score is 65%.

6. Is there a prerequisite for Analytics-Con-202?

Salesforce currently lists no prerequisite for the certification.

7. Which Analytics-Con-202 domain has the highest weighting?

Agentic Experiences accounts for 25% of the exam, followed by Data Setup at 20% and Embedding, Cross-Cloud & Interoperability at 20%.

8. What should I study about Data 360?

Focus on Data 360 data integration, management, preparation, modeling, semantic models, and selecting suitable data architecture or processing approaches.

9. What is Agentic Analytics?

For this certification, candidates need to understand how generative AI and the Analytics Agent support data exploration, insight generation, alerts, and notifications within Tableau Next.

10. What are Tableau Next semantic models?

Semantic models provide the analytical structure used to organize and interpret business data, including concepts needed for consistent analytics and AI-assisted experiences.

11. Does Analytics-Con-202 cover Salesforce integration?

Yes. The exam includes Tableau Next integration with Salesforce analytics platforms, cross-platform workflows, Tableau Next Apps for Salesforce, Marketplace offerings, and developer tools/APIs.

12. Are Tableau Next dashboards included in the exam?

Yes. Visualizations & Dashboards represents 15% of the exam and includes visualization best practices and dashboard actions.

13. Does the exam cover workspace management?

Yes. Managing Workspaces & Orgs represents 10% and includes Personal Orgs, asset management/sharing, and asset deployment.

14. What Salesforce resources can I use to prepare?

Salesforce recommends a combination of hands-on experience, training, Trailhead, and self-study. The official guide specifically points candidates toward the Prepare for Your Salesforce Tableau Next Consultant Certification Trailhead path.

15. How should I prepare for Analytics-Con-202?

A practical preparation approach is to study the official exam domains, build hands-on familiarity with Tableau Next and Data 360, review Agentic Analytics concepts, and use practice questions to identify weak areas before the exam. Salesforce recommends combining hands-on experience with training and self-study.