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| Section | Objectives |
|---|---|
| Topic 1: Data Management and Preparation | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
| Topic 2: Einstein Discovery | - Insights interpretation and recommendations - Model building and predictions |
| Topic 3: Data Integration and Connectivity | - Connecting external data sources - Data synchronization and refresh strategies |
| Topic 4: Dashboard and App Development | - Interactive filters and user experience optimization - Lens creation and dashboard design |
| Topic 5: Security and Access | - Row-level security and sharing rules - App and dataset permissions |
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NEW QUESTION # 55
A CRM Analytics consultant at Cloud Kicks is trying to upload data using an External Data API and the CSV file with the data wasuploaded successfully. Upon analyzing the data using a lens, they find they are unable to perform any mathematical operations as all the data and fields are treated as dimensions.
What is causing the problem?
Answer: A
NEW QUESTION # 56
Cloud Kicks (CK) has a dashboard that is still showing 10 former account managers. CK has since expanded, and some have moved from their former roles. CK wants the dashboard to reflect current account managers and stay up to date as new team members join.
How should the CRM Analytics developer accomplish this?
Answer: C
Explanation:
TheRepeater Widgetin CRM Analytics allows dynamic updates to be reflected on the dashboard automatically as data flows are updated. This means that as new account managers join or existing account managers change roles, the data in the widget will automatically reflect the latest information without manual updates. This is the most efficient way to keep the dashboard up-to-date with the current list of account managers.
Reference: The functionality ofRepeater Widgetsand their ability to dynamically update based on live data is covered in theWave Analytics Explorerdocumentation, particularly when dealing with team structures and dynamic datasets.
NEW QUESTION # 57
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
Answer: A
Explanation:
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.
NEW QUESTION # 58
consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.
Which action should the consultant take?
Answer: C
Explanation:
Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here's the rationale:
* Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired.
* Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions.
* Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It's about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making.
By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client's business strategies and operational realities.
NEW QUESTION # 59
What are various ways to incorporate blank space in a CRM Analytics dashboard?
Answer: C
Explanation:
Incorporating blank space into a CRM Analytics dashboard can be achieved effectively through the following methods:
* Cell Spacing Layout Property: This allows for consistent spacing between cells, helping to create a visually organized and less cluttered dashboard.
* Increasing Dashboard Granularity via Columns: Using blank columns as a method to create deliberate space can help in visually separating different dashboard elements, enhancing readability and focus.
These methods ensure that the dashboard is not only functional but also aesthetically pleasing and easy to navigate.
NEW QUESTION # 60
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