Introduction to Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) Exam
The course Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) teaches you how to explore your data from your desktop or mobile device through lenses and interactive dashboards, and how to share your insights in Chatter. Learn how to change measures, classes, and filters quickly; choose the best visualisations for your information; and drill down to display data. You'll also learn how to quickly load additional data from a CSV file, which you can use Einstein Analytics' power to start exploring right away. In the Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) Test, the Salesforce Mobile and Desktop Exploration is all about an agile mindset and applying agile to business analytics fundamentals and techniques.
Salesforce ANC-101 Exam Overview:
| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Einstein Analytics and Discovery Consultant Exam |
| Exam Number: | ANC-101 |
| Related Certifications: | Salesforce Certified Administrator Salesforce Certified Data Analyst Salesforce Certified Platform App Builder |
| Exam Duration: | 105 minutes |
| Passing Score: | 65-68% |
| Exam Price: | $200 USD |
| Exam Format: | Multiple Choice, Multiple Select |
| Available Languages: | Japanese, French, Spanish, German, English |
| Certificate Validity Period: | 1 year |
| Real Exam Qty: | 60-65 |
| Recommended Training: | Trailhead Einstein Analytics & Discovery Consultant Prep |
| Exam Registration: | Salesforce Certification Registration (Webassessor) |
| Sample Questions: | Salesforce ANC-101 Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Pearson VUE) |
| Pre Condition: | No strict prerequisites required, but Salesforce Administrator or Platform App Builder certification is recommended. |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/analyticsanddiscoveryconsultant |
How to study the Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) Exam
Anyone, who is planning to write Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) Exam, should focus on their study material. So one of the questions is the platform from where they can get the right study material for their preparation. To get the right or valid material from online sources, they can refer some of the authorized websites. We understand its very difficult to get a genuine website from where student or participant can learn the right things so we recommend taking the ANC101 practice exams as they are the best source of preparation. We have already defined all of the course content so if you tried to explore it based on the course content it could be very easy for you to plan your study and prepare notes accordingly.
Preparation and study materials for the Salesforce Mobile and Desktop Exploration in Einstein Analytics (ANC101) Exam are provided by several online platforms including the Salesforce Trailhead official website for free and by certificate-questions in the form of ANC101 exam dumps. Interested candidates can simply search online with the name of the exam and can find learning materials. In order to use the material provided by Salesforce Trailhead, visit the links in the reference section of this document.
For more info read reference:
Salesforce Official Website Preparation Material and prerequisites FAQs and Guide
Salesforce ANC-101 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Analytics Discovery and Insights | - Predictive modeling usage - Einstein Discovery concepts |
| Topic 2: Data Security and Access | - Security predicates - Sharing models and permissions - Row-level security |
| Topic 3: Data Modeling and Recipes | - Data transformation logic - Dataflows and recipes |
| Topic 4: Analytics Dashboard Design | - User experience and interactivity - Filters and bindings - Dashboard components and widgets |
| Topic 5: Data Management | - Data transformation and preparation - Data sources and ingestion - Dataset design and modeling |

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