Salesforce Certified Tableau CRM and Einstein Discovery Consultant Exam Dumps & Practice Test Questions
Question 1:
A consultant is developing a model to predict customer churn for a company. The goal is to identify customers who are likely to cancel or not renew their subscriptions. The dataset includes a field named “Churn Reason,” but this field is populated only for customers who have already churned, leaving it empty for active customers. The consultant must determine how to handle the “Churn Reason” field to ensure effective training of the churn prediction model.
What is the most suitable preprocessing step the consultant should follow before building the model?
A. Mark the “Churn Reason” field as sensitive due to potential private customer data exposure.
B. Replace missing values in the “Churn Reason” field with a placeholder like “No reason provided.”
C. Exclude the “Churn Reason” field from the dataset entirely.
D. Remove all active customers from the training data because their “Churn Reason” is missing.
Correct answer: B
Explanation:
In this scenario, the “Churn Reason” field is only populated for customers who have already churned, and is missing for active customers. This creates an issue because the model needs to learn from data that can be generalized to all customers, not just those who have already churned. When training a predictive model, it’s crucial to handle missing values appropriately, especially when the missingness itself is related to the target variable, in this case, churn.
Here’s why Option B is the most suitable preprocessing step:
Option B: Replace missing values in the “Churn Reason” field with a placeholder like “No reason provided.”
The "Churn Reason" field is a key feature in understanding why a customer churns. However, for active customers, the reason for churn is missing. A common approach in machine learning when dealing with missing data is to impute these missing values. In this case, imputing the missing values with a placeholder like “No reason provided” makes sense because:
It allows the model to include active customers in the dataset, maintaining a balance between churned and active customers.
The placeholder value "No reason provided" will enable the model to treat these customers as having no known reason for churn, which might be a useful distinction. This allows the model to potentially learn patterns such as "no reason provided" being linked to certain behaviors (e.g., more loyal customers or less engaged users).
This approach maintains a representative dataset, helps avoid bias in training, and ensures the model can make predictions for both active and churned customers.
Why the other options are less suitable:
Option A: Mark the “Churn Reason” field as sensitive due to potential private customer data exposure.
While the “Churn Reason” field may contain sensitive information (depending on what reasons are included), privacy concerns are not the primary issue in this case. The challenge here is to handle the missing data effectively for building the churn prediction model. Privacy issues can certainly be addressed later during the model’s deployment, but they do not directly impact how the data is preprocessed for training.
Option C: Exclude the “Churn Reason” field from the dataset entirely.
Excluding the “Churn Reason” field from the dataset might seem like an easy solution, but it would result in losing valuable information for the churn prediction model. Even though the field is missing for active customers, it still holds useful information for churned customers. If excluded entirely, the model would not be able to leverage this feature, which may hinder its ability to make accurate predictions. The better approach is to impute the missing values or handle them appropriately.
Option D: Remove all active customers from the training data because their “Churn Reason” is missing.
This approach would result in biasing the training data. If all active customers (those without a churn reason) are removed, the model would only learn from customers who have already churned, potentially making the model overfit to the churned data and failing to generalize well to active customers. Active customers are important for predicting churn, as they represent a large portion of the population and should be included in the model training.
To ensure effective training of the churn prediction model and avoid losing important data or introducing bias, the most suitable approach is to replace missing values in the “Churn Reason” field with a meaningful placeholder like "No reason provided." This ensures that the model has a complete dataset with both active and churned customers, and the placeholder can help the model understand the difference between customers with and without known churn reasons. Therefore, the correct answer is B.
Question 2:
A small Business Intelligence (BI) team is overwhelmed with an increasing number of dashboard creation requests from multiple departments. They are considering using layout templates to speed up the process while ensuring dashboards remain high quality and consistent.
What are two main benefits of using layout templates in a BI platform for dashboard development? (Choose two)
A. Layout templates provide a consistent structure, reducing time spent on designing dashboards from scratch.
B. Layout templates are static and cannot be modified, enabling faster deployment.
C. Layout templates ensure uniform user experience across all dashboards.
D. Layout templates incorporate best practices for dashboard design, ensuring proper arrangement of KPIs and filters.
Correct answers: A and C
Explanation:
The use of layout templates in a Business Intelligence (BI) platform offers several benefits to the team developing dashboards. These templates streamline the creation process, ensuring dashboards are not only consistent but also meet the team's design standards.
Option A: Layout templates provide a consistent structure, reducing time spent on designing dashboards from scratch.
One of the primary benefits of using layout templates is that they offer a predefined structure for the dashboards. This means that the BI team does not need to start from scratch every time a new dashboard is requested. Instead, they can reuse the layout, adjusting the contents to fit the data and purpose of the specific dashboard. This speeds up the dashboard creation process and ensures consistency across multiple dashboards, particularly when working with a high volume of requests.
This efficiency reduces the time and effort spent on design, allowing the team to focus more on data analysis and customization of the dashboard's content rather than its structure.
Option C: Layout templates ensure uniform user experience across all dashboards.
Using layout templates helps maintain a consistent design across all dashboards, which is crucial for creating a uniform user experience. When users across different departments access dashboards, they expect similar structures and layouts to reduce the learning curve and improve usability. By employing templates, the team ensures that users can quickly navigate through different dashboards without being confused by varying designs or layouts.
A uniform user experience enhances the effectiveness of the dashboards, ensuring that the key performance indicators (KPIs) and insights are easily accessible and interpreted consistently, regardless of which department requests the dashboard.
Why the other options are less suitable:
Option B: Layout templates are static and cannot be modified, enabling faster deployment.
While layout templates provide consistency, saying that they are "static and cannot be modified" is incorrect. In most BI platforms, layout templates can be modified to some extent, depending on the specific needs of the department or the dashboard's objectives. The ability to customize the templates ensures that the design remains flexible while also maintaining the benefits of consistency. The static nature of templates does not inherently enable faster deployment because flexibility and adjustments to meet specific needs are often required.
Option D: Layout templates incorporate best practices for dashboard design, ensuring proper arrangement of KPIs and filters.
While some layout templates might be designed with best practices in mind, not all templates necessarily incorporate the best practices for dashboard design. This would depend on the quality and design principles followed when creating the templates. Some templates may indeed be designed to follow best practices for the arrangement of KPIs, filters, and other visual elements, but this is not a universal guarantee for all templates.
The primary benefit of templates is that they provide structure and consistency, but whether they adhere to best practices depends on how they were designed and whether the templates are created with user needs in mind. Therefore, while this may be a benefit of some templates, it cannot be assumed to be true for all.
The most significant benefits of using layout templates are the consistency they provide in both structure and user experience, and the time savings they offer by eliminating the need to design dashboards from scratch. This makes Option A and Option C the most suitable answers.
Question 3:
A company is using Tableau CRM and wants to set up row-level security so that users in an Account Team can only view Opportunities related to their assigned Accounts. The company wants to ensure secure access control, but only allow members of the Account Team to view relevant Opportunities.
Which two configurations should be implemented in Tableau CRM to meet this requirement? (Choose two)
A. Create a master-detail relationship between Account and Opportunity in Salesforce to automatically inherit sharing rules.
B. Utilize Tableau CRM sharing inheritance to propagate Salesforce sharing rules to the datasets.
C. In the dataflow, pull in the AccountTeamMember object, join it with the Opportunity dataset using AccountId, and apply a security predicate: 'AccountTeamMember.UserId' == "$User.Id".
D. In the dataflow, pull in the OpportunityTeamMember object, join it with the Opportunity dataset using OpportunityId, and apply a security predicate: 'OpportunityTeamMember.UserId' == "$User.Id".
Correct answers: C and D
Explanation:
In Tableau CRM, row-level security ensures that users can only access data that they are authorized to view. For this scenario, where the goal is to allow Account Team members to view Opportunities related only to their assigned accounts, the solution needs to be based on the relationship between the Account and Opportunity and the membership of the Account Team.
Option C: In the dataflow, pull in the AccountTeamMember object, join it with the Opportunity dataset using AccountId, and apply a security predicate: 'AccountTeamMember.UserId' == "$User.Id".
This option is a correct configuration because it directly addresses the need for row-level security based on user access. By pulling in the AccountTeamMember object and joining it with the Opportunity dataset, we can ensure that only users who are part of the Account Team for a specific Account can see Opportunities related to that Account.
The security predicate 'AccountTeamMember.UserId' == "$User.Id" ensures that the security is applied such that each user can only access the Opportunities related to Accounts they are assigned to. This is a crucial step for enforcing row-level security based on user membership in the Account Team, ensuring that each user only sees data they are authorized to view.
Option D: In the dataflow, pull in the OpportunityTeamMember object, join it with the Opportunity dataset using OpportunityId, and apply a security predicate: 'OpportunityTeamMember.UserId' == "$User.Id".
This option is also a valid configuration, though it addresses a slightly different relationship. Here, the OpportunityTeamMember object is used to define row-level security for users based on their role in the Opportunity Team. By joining the OpportunityTeamMember object to the Opportunity dataset and applying the security predicate 'OpportunityTeamMember.UserId' == "$User.Id", the system ensures that users can only access Opportunities where they are a member of the Opportunity Team.
While Option C focuses on the Account Team, Option D focuses on the Opportunity Team, which is another valid method for controlling access at the Opportunity level.
Why the other options are less suitable:
Option A: Create a master-detail relationship between Account and Opportunity in Salesforce to automatically inherit sharing rules.
While creating a master-detail relationship between Account and Opportunity may help establish a strong data model, it does not directly address row-level security in Tableau CRM. Sharing rules in Salesforce are relevant for access control at the record level in Salesforce, but they do not directly translate into Tableau CRM’s dataset-level security. This option alone will not be sufficient for enforcing row-level security in Tableau CRM, as it doesn't involve security predicates or dataflow configurations that specifically address user access control in the BI platform.
Option B: Utilize Tableau CRM sharing inheritance to propagate Salesforce sharing rules to the datasets.
Sharing inheritance allows sharing rules from Salesforce to be applied to Tableau CRM datasets, but it is typically used for more general access controls and may not be granular enough to handle row-level security as required in this scenario. This approach doesn't specifically control access at the row-level based on the user's relationship with the Account or Opportunity, as required by the scenario. It focuses more on the inheritance of general sharing rules rather than fine-grained control of which records (e.g., Opportunities) a user can access.
To ensure that users in the Account Team can only view Opportunities related to their assigned Accounts, and that row-level security is properly implemented, the most appropriate configurations are Option C and Option D. These methods directly apply the correct security predicates to control access based on user roles and relationships with the data.
Question 4:
A shipping company has created a dataset in Tableau CRM that includes budget data for regions and months in the first half of 2018. However, some region-month combinations are missing from the dataset. The company wants to build a lens showing the total budget per region for each month, ensuring that all region-month combinations are included, even when no data exists for certain combinations.
What should the Tableau CRM consultant recommend to ensure that all combinations are represented in the lens, even if there is no data for them?
A. Use a "Compare Table" and apply the "Running Total" function on a custom column.
B. Create a "Compare Table" and activate the "Show Summary" option.
C. Write a SAQL query that uses the "fill" statement with the "partition" option to fill missing combinations.
D. Manually insert rows for missing combinations into the dataset with a SAQL query.
Correct answer: C
Explanation:
In this scenario, the goal is to ensure that all region-month combinations are represented in the lens, even when some of them do not have data in the dataset. Tableau CRM needs to fill in these missing combinations to show a complete picture of the budget for every region for every month.
Option C: Write a SAQL query that uses the "fill" statement with the "partition" option to fill missing combinations.
This is the best solution because it directly addresses the issue of missing data in the dataset. By using the "fill" statement in a SAQL query with the "partition" option, you can fill the missing combinations of region-month pairs by inserting null values for those combinations. This ensures that all combinations are included, even if no data exists for some of them.
The "fill" statement in SAQL can be used to generate missing data points in a time series or other grouped data, ensuring that there are no gaps in the analysis.
The "partition" option allows the query to fill missing values for each region separately, ensuring that the lens shows budget data for all regions and months, even when some combinations don't have data.
By using this method, you can ensure that Tableau CRM will include every region-month combination in the lens, even if there is no existing data for some combinations.
Why the other options are less suitable:
Option A: Use a "Compare Table" and apply the "Running Total" function on a custom column.
While the "Running Total" function is useful for cumulative calculations, it does not address the issue of missing region-month combinations. A Running Total would aggregate existing data but would not fill in the missing combinations where no data is available. It’s not the right approach for this scenario, as it doesn't ensure that all combinations are included in the lens, particularly when some combinations are missing data.
Option B: Create a "Compare Table" and activate the "Show Summary" option.
The "Show Summary" option in a Compare Table provides a summary of the data, but it does not specifically fill missing data. It’s useful for getting an overview or aggregating data but doesn’t solve the problem of missing region-month combinations in the dataset. This option won’t create the missing rows needed for the lens to show all combinations.
Option D: Manually insert rows for missing combinations into the dataset with a SAQL query.
While manually inserting rows into the dataset could technically fill the missing combinations, this is not an efficient or scalable solution. It requires significant manual effort to create and insert each missing combination, which is not practical, especially if the dataset is large or if the missing combinations are dynamic. Option C provides a much more automated and efficient way to handle this problem.
The correct and most efficient method is to use a SAQL query with the "fill" statement and the "partition" option to automatically fill the missing region-month combinations. This ensures that all combinations are represented in the lens, even if there is no data for some of them, making Option C the best choice.
Question 5:
A development team has successfully completed a dataflow in Tableau CRM. However, when querying a field in the output dataset, no data appears for that field, even though other fields return results.
What is the most probable reason for the blank field in the query, even though the dataflow has executed without error?
A. The Integration User does not have access to the field in the dataset.
B. The field is empty in the original Salesforce data.
C. The user executing the dataflow lacks field-level access to the field.
D. Row-level security configurations in the Security User Profile restrict access to the field.
Correct answer: B
Explanation:
The situation described suggests that one specific field in the output dataset does not contain any data, while the other fields return results as expected. Since the dataflow has executed without error, we can rule out issues related to dataflow execution or errors in processing.
Let's analyze the possible reasons:
Option A: The Integration User does not have access to the field in the dataset.
This option is unlikely to be the cause of the blank field. If the Integration User did not have access to the field, this would typically result in an error or the field being excluded from the dataset entirely. Since the field is present but blank, it indicates the field exists in the dataset but contains no data.
Option B: The field is empty in the original Salesforce data.
This is the most probable cause of the issue. If the field in the original Salesforce data is empty (or does not contain any valid data), it will result in a blank field in the output dataset. Dataflows in Tableau CRM process the data directly from the source (in this case, Salesforce), and if there is no data in the field, the corresponding field in the output dataset will also be blank, even though other fields might contain data.
This could happen if the field is optional, and in the cases where data is present, it is populated, but for certain records, it remains empty.
Since the dataflow executed without errors, it confirms that the issue is not related to processing or permissions but rather the absence of data in that specific field.
Option C: The user executing the dataflow lacks field-level access to the field.
If the user executing the dataflow lacked field-level access to the field, this would typically result in an error or the field being excluded from the dataset. However, since the field is part of the dataset and is showing up as blank, it’s unlikely that field-level access is the issue.
Option D: Row-level security configurations in the Security User Profile restrict access to the field.
While row-level security could restrict access to specific records for a user, it would not typically result in the field being blank for all users. Row-level security would prevent access to the entire record or specific rows of data, but it wouldn't cause a field to be blank unless no data was available for the user due to the security constraints. This is a less likely cause for the issue, especially if the issue is observed for all users and not just a specific security profile.
The most likely reason for the blank field is that the field is empty in the original Salesforce data. If no data exists in that field for the relevant records, the output dataset will reflect that by showing a blank field. Therefore, Option B is the correct choice.
Question 6:
Universal Containers has created a Tableau CRM dashboard for Sales Managers, showing the Year-over-Year (YoY) growth of their customer base. The YoY Growth formula is:
YoY_Growth=(This_Year−Last_YearLast_Year)×100YoY\_Growth = \left( \frac{This\_Year - Last\_Year}{Last\_Year} \right) \times 100YoY_Growth=(Last_YearThis_Year−Last_Year)×100
The dashboard works fine when both years have data, but when there’s no data for "Last Year," the formula returns null values, making the visualization unclear.
The Sales Managers want to show 100% as the default value instead of null when "Last Year" data is missing.
Which function should be used to replace null values with 100% in the formula?
A. coalesce()
B. number_to_string()
C. substr()
D. replace()
Correct answer: A
Explanation:
In this scenario, we want to replace null values with a default value of 100% when the "Last Year" data is missing, which is causing the formula to return a null value. The key here is handling null values in the formula to prevent them from affecting the calculation and providing a meaningful default value (100%).
Option A: coalesce()
The coalesce() function is specifically designed to handle null values in Tableau CRM and other SQL-like query languages. The function returns the first non-null value in a list of expressions. If Last Year data is null, it will return the specified default value (in this case, 100%) instead of returning null.
For example, you can modify the formula to use coalesce() as follows:
YoY_Growth = (This_Year - coalesce(Last_Year, This_Year)) / coalesce(Last_Year, This_Year) * 100
This formula will replace the null value of Last_Year with This_Year (or any other desired value), ensuring the YoY growth calculation proceeds smoothly without resulting in null values.
Thus, Option A: coalesce() is the correct choice.
Option B: number_to_string()
The number_to_string() function is used to convert numbers to string values, which is not relevant to this situation. We are trying to replace null values in a numerical calculation, not convert data types, so this option is not applicable.
Option C: substr()
The substr() function is used to extract a substring from a string based on given indices. This function is not relevant for replacing null values in a numerical formula, making this option incorrect.
Option D: replace()
The replace() function is used to replace substrings in string fields, which is not applicable in this case. The replace() function does not work for handling null values in numerical fields, so this option is not suitable.
The correct function to use for replacing null values with a default value of 100% in the formula is coalesce(). This ensures that missing data for "Last Year" will not cause the formula to return null values and will instead show a meaningful default result. Therefore, the correct answer is Option A: coalesce().
Question 7:
Universal Containers has set up Data Sync (Replication) in Tableau CRM to extract Salesforce object data into replicated datasets. After syncing, the admin compares the sum of a numeric field in the Salesforce object with the sum in the synced dataset, but the numbers don’t match.
What are two likely reasons for this discrepancy between the sums? (Choose two)
A. The replicated dataset does not capture updates made by triggers in Salesforce.
B. Records permanently deleted in Salesforce are not included in the replicated dataset.
C. Workflow rules that modify fields may not be reflected immediately in the replicated dataset.
D. Formula fields are not included in the replicated dataset during data sync.
Correct Answers: B and C
Explanation:
When using Data Sync (Replication) in Tableau CRM to extract data from Salesforce into replicated datasets, it is important to ensure that the sync process accurately reflects the latest updates and data. Discrepancies between the sums in Salesforce and the replicated dataset can occur for several reasons, often related to the timing of data changes or the types of data that are included in the sync.
Option A: The replicated dataset does not capture updates made by triggers in Salesforce.
This option is not likely to be the cause of the discrepancy. Data Sync in Tableau CRM does capture changes made by triggers in Salesforce. However, depending on the type of trigger (e.g., if it's after insert, update, or delete), there might be slight delays before these changes are reflected in the replicated dataset. Nevertheless, this scenario is typically not the main cause of mismatched sums. The replication process does capture updates made by triggers.
Option B: Records permanently deleted in Salesforce are not included in the replicated dataset.
This is a likely reason for the discrepancy. If records are permanently deleted from Salesforce (rather than just being marked as deleted or archived), they will not be included in the replicated dataset. The sync process does not include permanently deleted records, meaning the summed values in Salesforce may still account for these records, whereas the synced dataset will not, causing the discrepancy.
Option C: Workflow rules that modify fields may not be reflected immediately in the replicated dataset.
This is another likely reason for the discrepancy. Workflow rules in Salesforce often trigger field updates or changes to records. However, these changes may not immediately appear in the replicated dataset, depending on when the sync process occurs. There can be a delay between the time a workflow rule modifies a record and the time that change is reflected in the replicated dataset. This can lead to mismatches in summed values if the sync hasn't captured the latest updates.
Option D: Formula fields are not included in the replicated dataset during data sync.
This is incorrect because formula fields are included in the replicated dataset. While it is true that formula fields are not directly editable in the replicated dataset (since they are calculated fields in Salesforce), the values derived from the formula fields are included in the data extracted during the sync. Therefore, this is not a likely cause of the discrepancy in the sums.
The most likely reasons for the discrepancy in the sums are:
Option B: Records permanently deleted in Salesforce are not included in the replicated dataset.
Option C: Workflow rules that modify fields may not be reflected immediately in the replicated dataset.
These two options address the possible issues related to the timing of data changes and the inclusion of certain types of records in the replicated dataset. Therefore, the correct answers are B and C.
Question 8:
A Tableau CRM consultant has finalized the design and received approval for the desktop layout of a dashboard. Now, they are focusing on building the mobile layout for the dashboard to ensure it displays correctly on mobile devices.
When designing the mobile layout, which three important aspects should the consultant keep in mind? (Choose three)
A. If there are multiple matching layouts, the one with the most specific device settings is selected, with ties broken by the most recently created layout.
B. A mobile layout is valid only when the device satisfies all properties defined in the layout settings.
C. If no mobile layout matches, the default layout (first defined) will be used.
D. In the absence of a matching mobile layout, an error message is displayed to the user.
E. Some widgets from the desktop layout might not be supported in the mobile layout.
Correct Answers: A, C, E
Explanation:
When designing a mobile layout in Tableau CRM (previously known as Einstein Analytics), there are several key considerations to ensure that the dashboard looks good and functions correctly on mobile devices. Let's break down each option:
Option A: If there are multiple matching layouts, the one with the most specific device settings is selected, with ties broken by the most recently created layout.
This is correct. Tableau CRM allows you to create multiple layouts for different devices (e.g., desktop, tablet, mobile). When there are multiple matching layouts, Tableau CRM will prioritize the layout with the most specific settings (e.g., a layout designed for a particular device or screen size). In the case of a tie, the most recently created layout is chosen.
Option B: A mobile layout is valid only when the device satisfies all properties defined in the layout settings.
This is incorrect. Tableau CRM does not require all properties of the device to be met for a layout to be valid. Instead, it looks for the best match based on device type and size. If there isn't a perfect match, it will fall back to the default layout.
Option C: If no mobile layout matches, the default layout (first defined) will be used.
This is correct. If no mobile-specific layout is defined, Tableau CRM will default to using the original desktop layout. This ensures that the dashboard is still usable on mobile devices, even if no specific mobile layout has been created.
Option D: In the absence of a matching mobile layout, an error message is displayed to the user.
This is incorrect. Tableau CRM does not display an error message if no matching mobile layout is found. Instead, it will simply use the default layout or the closest match available.
Option E: Some widgets from the desktop layout might not be supported in the mobile layout.
This is correct. Not all widgets or features from the desktop layout may be compatible with the mobile layout. For example, certain types of charts, complex visualizations, or interactivity may not render well on smaller screens. The consultant should ensure that the mobile layout is optimized for mobile devices, which may include resizing or removing certain elements.
The three key considerations when designing a mobile layout for Tableau CRM are:
A: The most specific layout based on device settings is chosen, with ties broken by the most recent layout.
C: If no mobile layout matches, the default layout will be used.
E: Some widgets from the desktop layout may not be supported or may need to be modified for mobile devices.
Thus, the correct answers are A, C, and E.
Question 9:
Universal Containers has enabled data sync (replication) in Tableau CRM to bring Salesforce object data into replicated datasets. The admin is now examining an existing dataflow to determine which transformation is responsible for defining the Salesforce objects and fields to be included in the replication process.
Which transformation in the dataflow defines the Salesforce objects and fields for extraction during the sync process?
A. edgemart
B. export
C. sfdcDigest
D. sfdcRegister
Correct Answer: D. sfdcRegister
Explanation:
In Tableau CRM (formerly Einstein Analytics), the sfdcRegister transformation is used to define the Salesforce objects and fields that will be included in the replication (data sync) process. This transformation registers the Salesforce objects that will be part of the sync and specifies which fields to extract from those objects.
Let’s break down the other options:
Option A: edgemart
Incorrect. An edgemart is a dataset that contains pre-aggregated or pre-processed data, and it's used in Tableau CRM for querying or analysis. It is not responsible for defining which Salesforce objects or fields are included in the sync process.
Option B: export
Incorrect. The export transformation is used to export data from a dataflow to an external location (e.g., another dataset or an external system), but it doesn’t define which Salesforce objects and fields will be synced.
Option C: sfdcDigest
Incorrect. The sfdcDigest transformation is responsible for extracting metadata (like objects and field types) from Salesforce. It helps in optimizing dataflow processes, but it does not define the actual objects and fields to be included in the replication process.
Option D: sfdcRegister
Correct. The sfdcRegister transformation is specifically used for registering Salesforce objects and fields to be replicated. It defines the objects and fields that are extracted during the data sync process. This is the transformation that connects Salesforce data to Tableau CRM for replication.
The sfdcRegister transformation defines the Salesforce objects and fields for extraction during the sync process in Tableau CRM. Therefore, the correct answer is D. sfdcRegister.
Question 10:
A company has implemented a machine learning model to predict customer retention rates. The model uses several customer attributes, but one of the critical features, "Contract Duration," is missing for some customers who have yet to finalize their contracts.
Which preprocessing strategy should the data scientist use to handle the missing "Contract Duration" values for customers without finalized contracts?
A. Impute missing values with the median contract duration of all customers.
B. Exclude all customers with missing "Contract Duration" values from the training dataset.
C. Replace missing values with a placeholder like "Not Available."
D. Use a predictive model to estimate the missing "Contract Duration" based on other customer attributes.
Correct Answer: D. Use a predictive model to estimate the missing "Contract Duration" based on other customer attributes.
Explanation:
In this scenario, the missing "Contract Duration" values represent a critical feature in the machine learning model. Here’s how to approach it:
Option A: Impute missing values with the median contract duration of all customers.
Partially Correct but not ideal in this case. While imputing missing values with the median can be a reasonable strategy in some situations (e.g., numerical columns with missing data), this is less optimal when the feature is essential to model predictions like customer retention. Simply replacing missing values with the median may not capture the specific relationship between contract duration and customer behavior.
Option B: Exclude all customers with missing "Contract Duration" values from the training dataset.
Incorrect. Excluding customers with missing values can lead to a loss of valuable data, especially if the number of customers missing this value is significant. This could introduce bias, as excluding this group might distort the dataset and its representation of the broader customer population.
Option C: Replace missing values with a placeholder like "Not Available."
Not Ideal. While using placeholders can sometimes be useful for categorical data, it's not the best strategy for numerical features like "Contract Duration." The placeholder would be treated as a separate category or value, which could negatively impact the performance of the machine learning model.
Option D: Use a predictive model to estimate the missing "Contract Duration" based on other customer attributes.
Correct. This is the best strategy. Since "Contract Duration" is a critical feature and may be correlated with other customer attributes, using a predictive model (such as regression or a decision tree) to estimate the missing values can preserve the integrity of the data and improve the model’s accuracy. This strategy leverages other available information to predict the missing values, making the imputation process more informed and potentially more accurate.
The most appropriate preprocessing strategy is D. Use a predictive model to estimate the missing "Contract Duration" based on other customer attributes. This ensures that the imputation process is more accurate and reflects the relationships in the data, making it a more effective approach for handling missing critical features.
