Exam: Analytics-Con-201 Dumps

Analytics-Con-201 Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam

1. Exam Overview
The Salesforce Certified CRM Analytics and Einstein Discovery Consultant (Analytics-Con-201) exam is intended for professionals who want to demonstrate skills in designing, implementing, and optimizing analytics solutions within the Salesforce ecosystem. Preparation focuses on transforming business data into meaningful dashboards, developing analytical applications, and using Einstein Discovery to uncover patterns and support data-driven decisions.
CertKingdom provides exam-preparation resources designed to help candidates review important concepts, practice exam-style questions, identify knowledge gaps, and build confidence before test day.

2. Topics Covered in Analytics-Con-201
Candidates should review the following subject areas and confirm their alignment with the current official exam guide.
- CRM Analytics fundamentals: Architecture, capabilities, use cases, and analytics applications.
- Data preparation and integration: Data sources, dataflows, recipes, data transformation, and data modeling.
- Datasets and data security: Dataset design, access permissions, security predicates, and sharing considerations.
- Dashboards and lenses: Dashboard creation, widgets, filters, bindings, interactions, and visual storytelling.
- Analytics design: Choosing suitable charts, KPIs, dimensions, measures, and visualizations for business needs.
- Einstein Discovery: Predictive insights, model creation, model evaluation, explanations, and recommendations.
- Deployment and administration: Application management, permissions, testing, maintenance, and troubleshooting.
- Business requirements: Translating stakeholder questions into analytics solutions and actionable insights.
These are broad preparation areas, not a verified official weighting breakdown.

3. Official exam domains and weightings
Salesforce’s published exam guide lists these domain weightings.

Salesforce Help
Exam domain Weight
Admin/Configuration 17%
Data Layer 23%
Security 16%
Analytics Dashboard Design 13%
Analytics Dashboard Implementation 19%
Einstein Discovery 12%
The official guide lists 60 multiple-choice questions, up to five unscored questions, 90 minutes, and a 65% passing score. Confirm the current details before publishing, as exam policies and guides can change.

Prepare for the Salesforce Analytics-Con-201 exam with CertKingdom's CRM Analytics and Einstein Discovery Consultant exam preparation resources. Review practice questions, study dataflows, datasets, SAQL, dashboard design, security, and Einstein Discovery models. Strengthen your knowledge with exam-style practice and focused revision to prepare for your Salesforce certification.

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- How do I pass the Analytics-Con-201 exam?
- Where can I find Analytics-Con-201 practice questions and answers?
- What are the latest Salesforce CRM Analytics Consultant exam topics?
- Are there free Analytics-Con-201 sample questions?
- What is the best study guide for Analytics-Con-201?
- How difficult is the CRM Analytics and Einstein Discovery Consultant exam?
- How long should I study for the Salesforce Analytics-Con-201 certification?
- What is the difference between CRM Analytics and Einstein Discovery?
- How do I prepare for SAQL, dashboards, and dataflows?
- Which Salesforce Trailhead modules help with Analytics-Con-201?
- What are the exam domains and passing score?
- How can I practice Salesforce CRM Analytics scenario-based questions?
- Where can I find an Analytics-Con-201 mock exam?
- What security concepts should I study for CRM Analytics?
- How do I understand Einstein Discovery models and predictions?

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QUESTION 1
Your organization is rolling out CRM Analytics to 150 sales and marketing users across three divisions. Each
division has different row-level security requirements based on their territory assignments, and you need to
ensure users only see opportunities and accounts relevant to their regions. The divisions currently use
custom sharing rules in Salesforce, but the data will be consolidated into a single CRM Analytics dataset that
combines data from all three divisions.
Which combination of approaches should you implement to meet these row-based security requirements
while maintaining ease of administration?

A. Use sharing inheritance on the dataset so that Salesforce object sharing rules automatically apply, and
configure security predicates as a fallback for territory-specific filtering
B. Implement security predicates only, removing reliance on Salesforce sharing rules to avoid conflicts
and ensure consistent filtering logic
C. Create three separate datasets—one per division—and use dataset sharing to control access, rather
than implementing row-level filters
D. Use only Salesforce role hierarchy inheritance without security predicates, as this replicates custom
sharing rules automatically in CRM Analytics
E. Implement field-level security (FLS) on the dataset columns and rely on record type sharing to enforce
row-level access

Answer: A

Explanation:
Correct Answer: Use sharing inheritance on the dataset so that Salesforce object sharing rules automatically
apply, and configure security predicates as a fallback for territory-specific filtering.
This approach leverages CRM Analytics' native sharing inheritance feature, which respects Salesforce sharing
rules and custom sharing applied to source objects. Security predicates then provide granular row-level filtering
for territory-specific or complex logic that sharing rules alone cannot achieve. This minimizes manual
configuration and keeps security logic synchronized with Salesforce.
Why the other options are incorrect:
* Option B: Relying solely on security predicates removes the advantage of sharing inheritance and creates
maintenance burden by requiring manual filter logic for every territory scenario.
* Option C: Splitting into three datasets is inefficient and prevents cross-division analytics and consolidation,
which contradicts the stated business need.
* Option D: Role hierarchy inheritance alone does not replicate custom sharing rules; you need explicit sharing
inheritance enabled on the dataset to do this.
* Option E: FLS is column-level security and does not provide row-level filtering; record type sharing is not a
row-level security mechanism in CRM Analytics.

QUESTION 2
A financial services firm is building an Einstein Discovery model to predict whether customers will renew
their insurance policies in the next quarter. They have gathered 18 months of historical renewal data with 42
features including policy age, customer tenure, claim history, premium amount, and product type. Initial
model accuracy is 78%, but stakeholders want to improve performance before deploying predictions to the
Lightning record pages of 500 sales representatives.
Which actions should you take to improve the model while respecting best practices for model refinement?

A. Review the Model Card to identify which features have the strongest influence on predictions, remove
low-impact features, and retrain the model with the refined feature set
B. Add all available customer demographic data to increase the feature count to over 100, as higher
feature volume always improves model accuracy
C. Increase the training data window to 36 months regardless of data quality, since more historical data
always improves predictions
D. Implement the current model immediately and monitor performance through the Model Card; adjust
features only after deployment if accuracy drops
E. Switch from a binary classification model to a multi-classification model to capture renewal likelihood in more
granular categories

Answer: A

Explanation:
Correct Answer: Review the Model Card to identify which features have the strongest influence on
predictions, remove low-impact features, and retrain the model with the refined feature set.
The Model Card shows feature importance and influence on predictions, allowing you to identify which variables
truly drive the model's decision-making. Removing low-impact or noisy features reduces overfitting, simplifies
the model, and often improves generalization performance. Retraining with this refined feature set is a
standard best practice in Einstein Discovery.
Why the other options are incorrect:
* Option B: Adding features indiscriminately introduces noise and dimensionality problems; feature quality
and relevance matter far more than quantity. This often degrades model performance.
* Option C: While more data can help, extending the window without assessing data quality or relevance can
introduce stale or irrelevant patterns that hurt predictions. You must validate data quality first.
* Option D: Deploying an 78% accuracy model to 500 users without investigation misses the opportunity to
improve performance before widespread adoption, increasing the risk of poor user adoption and trust issues.
* Option E: The problem is binary (renew or not renew), so multi-classification is mismatched to the business
question and would not improve prediction of the binary outcome.

QUESTION 3
Your organization has developed a CRM Analytics dashboard used by 200+ sales managers to track pipeline
and forecast accuracy. The dashboard contains eight compare tables with time-series analysis, complex
queries, and multiple selection interactions. After recent user feedback and dashboard expansion, you notice
that the dashboard takes 8-12 seconds to load during peak business hours, and some interactions feel sluggish.
Which combination of actions should you take first to diagnose and address the performance issue?

A. Use Dashboard Inspector to profile query execution times and identify slow queries, then assess
whether query type changes, query result limits, or widget-level parameter optimization can reduce latency
B. Immediately convert all compare tables to simple single-table queries to reduce complexity, regardless
of whether they show different analysis
C. Increase the CRM Analytics compute resources or request a platform upgrade without analyzing the
actual query performance bottleneck
D. Remove all time-series analysis from the dashboard, as time-series functionality is inherently slow and
should only be used in dedicated time-series dashboards
E. Disable all selection interactions and filter widgets to reduce the computational load on the backend

Answer: A

Explanation:
Correct
Answer: Use Dashboard Inspector to profile query execution times and identify slow queries, then
assess whether query type changes, query result limits, or widget-level parameter optimization can reduce
latency.
Dashboard Inspector is the built-in tool in CRM Analytics for monitoring and optimizing query performance. It
shows execution times for each widget and query, helping you pinpoint which specific queries are causing
delays. Once you identify slow queries, you can optimize them by adjusting query types, setting tighter result
limits, or refining widget-level parameters—often without hardware upgrades.
Why the other options are incorrect:
* Option B: Compare tables are the appropriate choice for multi-table analysis; removing them without
understanding why they're slow discards valuable functionality and analysis capability based on speculation,
not diagnosis.
* Option C: Increasing resources before profiling wastes budget and doesn't address the root cause.
Performance issues are usually query-logic problems, not resource scarcity.
* Option D: Time-series analysis is a valid and efficient CRM Analytics feature when used correctly. Removing
it unnecessarily eliminates analytical capability; the issue is query design, not the time-series feature itself.
* Option E: Selection interactions and filters are essential for user experience and exploration. Removing them
degrades the dashboard's usability without addressing performance; the issue lies in query optimization, not
the interaction framework.

QUESTION 4
Your organization manages customer data across Salesforce, a data warehouse, and an on-premises ERP
system. You are designing the data layer for a CRM Analytics application that will support executive
dashboards, sales analytics, and customer support reporting. The data warehouse is authoritative for
customer financial data, while Salesforce is authoritative for customer interactions and opportunities. Data
must refresh daily, and you need to support ad hoc transformations without deploying custom code.
Which data ingestion and transformation strategy best aligns with your requirements?

A. Use Data Manager to connect to both Salesforce and the data warehouse, create datasets from both
sources, and use dataflows/recipes to perform daily transformations and data blending
B. Ingest all data through the ERP system only, transform it there, and sync the final dataset to CRM
Analytics once per day using a single data sync
C. Create a direct JDBC connector to the data warehouse and ingest raw data without transformation,
relying on dashboard-level filtering to meet reporting needs
D. Use the CRM Analytics API to push data from Salesforce directly to the application, bypassing Data
Manager and dataflows
E. Create separate disconnected datasets for Salesforce and the data warehouse, then join them in
dashboards using widget-level parameters only

Answer: A

Explanation:
Correct
Answer: Use Data Manager to connect to both Salesforce and the data warehouse, create datasets
from both sources, and use dataflows/recipes to perform daily transformations and data blending.
Data Manager is the primary tool for extracting and loading data from multiple sources into CRM Analytics.
Dataflows and recipes enable ad hoc transformations and data blending without custom code, and they support
daily refresh schedules. This approach accommodates your multi-source environment while maintaining
flexibility for transformation logic.
Why the other options are incorrect:
* Option B: Routing all data through the ERP system contradicts your stated requirement that Salesforce is
authoritative for interactions and opportunities. It also creates an unnecessary intermediary and single point
of failure.
* Option C: Direct JDBC connectors bypass Data Manager and prevent proper dataset structure and refresh
scheduling. Ad hoc transformations and blending are not possible without a data management layer;
dashboard filtering is insufficient for data preparation.
* Option D: The CRM Analytics API is designed for data retrieval and querying, not data ingestion. It is not a
suitable replacement for Data Manager for loading and transforming data.
* Option E: Disconnected datasets prevent meaningful data blending and consolidation. Widget-level
parameters cannot replace actual data joins; this approach defeats the purpose of a unified data layer and
severely limits analytical capability.

QUESTION 5
Your organization is implementing CRM Analytics for a global sales organization with 2,500 users. Finance
requires row-level security so that each regional sales manager can see only opportunities and accounts in
their region. You are designing the security model and need to recommend the best approach.
Which two of the following should you implement to meet this requirement?

A. Use sharing inheritance from the source Salesforce object to automatically enforce region-based access
B. Create a dataset security predicate using a dimension field that matches the user's region assignment in a security mapping table
C. Manually grant dataset access to each regional sales manager through individual dashboard sharing rules
D. Implement field-level security on the CRM Analytics dataset to hide region fields from non-admin users
E. Create separate datasets for each region and assign permission sets to control which dataset each user can access

Answer: A, B

Explanation:
Correct answers: Using sharing inheritance and creating security predicates.
Row-based security in CRM Analytics is best implemented through two complementary mechanisms. Sharing
inheritance automatically applies the source object's Salesforce sharing rules to the dataset, providing a
foundation for security. Security predicates use dimension fields (such as a Region field) to filter data based on
user attributes stored in security mapping tables, enabling fine-grained row-level control without manual
dashboard sharing.
Why the other options are incorrect:
Manually granting dataset access through individual dashboard sharing does not enforce row-level filtering—it
only controls who can view the dashboard, not which rows they see. Field-level security in CRM Analytics hides
columns from the entire user population and does not provide row-level filtering based on user identity.
Creating separate datasets per region is administratively burdensome and violates the principle of single
dataset governance; it also prevents cross-regional analysis when needed.

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FAQ Section

15 Frequently Asked Questions (FAQs)


1. What is the Salesforce Analytics-Con-201 exam?
The Analytics-Con-201 exam is the Salesforce Accredited Agentforce Communications Professional exam. It assesses knowledge of customer discovery, solution design, communications product catalogs, order management, and implementation delivery.

2. What topics are covered in the Analytics-Con-201 exam?
The official exam outline covers Discovery (24%), Planning and Design (60%), and Delivery (16%).

3. How many questions are on the Analytics-Con-201 exam?
The published exam guide specifies 55 multiple-choice or multiple-select questions, with up to five additional unscored questions.

4. What is the passing score for Analytics-Con-201?
The official exam guide lists a passing score of 56%. Confirm current requirements with Salesforce before scheduling.

5. How long is the Analytics-Con-201 exam?
The published exam duration is 90 minutes.

6. How much does the Salesforce Analytics-Con-201 exam cost?
The published registration fee is US$150, plus applicable taxes. Check the official registration page for current pricing and retake policies.

7. What is the best way to prepare for Analytics-Con-201?
Review the official exam guide, complete recommended Salesforce learning materials, gain practical experience, and use practice assessments to identify knowledge gaps.

8. Is the Analytics-Con-201 exam difficult?
Difficulty depends on your experience with Salesforce communications solutions, product modeling, order workflows, and implementation architecture. Practical scenario-based preparation can help.

9. What is Enterprise Product Catalog (EPC)?
EPC is a product catalog capability used to model and manage commercial products and their relationships. Understanding scalable product catalog design is part of Analytics-Con-201 preparation.

10. What should I study about Industries CPQ?
Focus on requirements-driven quoting and order capture, product configuration, commercial product decomposition, and how these processes fit into a communications solution.

11. What is order orchestration in Agentforce Communications?
Order orchestration coordinates the activities needed to fulfill a customer order. For Analytics-Con-201, study how to design fulfillment flows that align with business requirements and architectural best practices.

12. Does Analytics-Con-201 cover data migration?
Yes. Candidates should understand data quality, migration planning, legacy systems, target architecture, data volumes, and strategies for moving business data reliably.

13. What does MACD mean in asset management?
MACD stands for Move, Add, Change, and Delete. It describes common changes to customer services and assets that candidates should understand when studying asset management flows.

14. Where can I find Analytics-Con-201 practice questions?
You can review legitimate practice assessments and study materials from training providers, alongside Salesforce Help and Trailhead resources. Choose materials that explain answers and align with the published objectives.

15. Where can I find official Analytics-Con-201 exam information?
Start with the official Salesforce Analytics-Con-201 exam guide and the Salesforce Trailhead Agentforce Communications learning trail.

Analytics-Con-201 Salesforce exam preparation resources for Agentforce Communications professionals, featuring practice questions, study guides, and online testing tools.