Esri EGMP2201 - Enterprise Geodata Management Professional 2201

Scenario Overview:

The wells feature class has one row per well.

The well_inspections table has one row for each inspection of a well.

Inspection dates from the well_inspections table need to be displayed as labels clustered around each well on the map.

The goal is to establish a connection between these two datasets without permanently joining them, as the data is being displayed dynamically (inspection dates are clustered around the wells).

Relates in Geodatabases:

A relate is a type of table association in which tables are linked by a common key field but remain separate.

Relates allow for dynamic queries to retrieve related records without duplicating or permanently associating the data.

Using a relate, you can query all inspection dates for a specific well dynamically, display them on the map as labels, and preserve the integrity of both the wells and inspections datasets.

(ArcGIS Documentation: Relates)

Alternative Options:

Option A: Join

A join merges two tables into one virtual table, based on a shared key. However, this approach is static and inappropriate for displaying dynamically clustered labels since the tables would need to be rejoined after every update.

Option C: Relationship Class

A relationship class is a more permanent association that enforces rules between two datasets. It is ideal for maintaining relationships between data but is unnecessary for dynamically labeling inspection dates on the map.

Thus, a relate is the most efficient and appropriate option for this scenario.

When users experience performance issues while adding data from child versions in a complex traditional version tree, it often indicates problems with thebase tables. TheAnalyze Base Tables For Selected Datasetsparameter is the most relevant in this case.

1. Role of Base Tables in Traditional Versioning

In traditional versioning, thebase tablestores the original data for the feature class or table. Changes made in child versions are tracked in delta tables (Adds and Deletes).

If the base table is not optimized (e.g., outdated statistics, fragmented indexes), performance can degrade when querying or rendering data.

2. Why Analyze Base Tables?

TheAnalyze Base Tables For Selected Datasetsparameter evaluates and updates the database statistics for the base tables to improve query optimization.

This process ensures the database query optimizer can make efficient decisions when retrieving data.

3. Why Not Other Options?

Include System Tables:

This analyzes geodatabase system tables, which are crucial for administrative tasks but unrelated to performance issues with user datasets.

Analyze Archive Tables For Selected Datasets:

This is specific to datasets with archiving enabled. There is no mention of archiving being used in this scenario.

Steps to Analyze Base Tables:

OpenArcGIS Proor use a Python script with theAnalyze Datasetstool.

Specify the datasets with performance issues.

Select theAnalyze Base Tables For Selected Datasetsparameter.

Run the tool and monitor the updated statistics.

References from Esri Documentation and Learning Resources:

Analyze Datasets Tool

Improving Query Performance

Conclusion:

TheAnalyze Base Tables For Selected Datasetsparameter should be used to update statistics and improve performance when adding data from child versions in traditional versioning.

ï‚· Understanding the Scenario:

Editors need to isolate their edits so that changes are not immediately visible to others.

Edits must bereviewed before becoming public, indicating a requirement for a structured approval process.

The organization aims to reduce the number of RDBMS users, which suggests centralized management of access and permissions.

ï‚· Editing Models Overview:

Branch Versioning:Designed for web-based workflows and does not require direct RDBMS access for each editor. However, edits made in branch versioning are inherently collaborative and are not isolated unless explicitly controlled through a branch-per-user workflow, which adds complexity.

Traditional Versioning:

Supports isolated editing through private versions.

Editors can create their own versions, make changes, and submit them for review by reconciling and posting to the default version.

Direct access to the RDBMS is centralized, reducing the need for individual RDBMS users.

Nonversioned Editing:Does not support isolated edits or versioned workflows, making it unsuitable for this scenario.

ï‚· Steps to Implement Traditional Versioning:

Register the feature class asversionedin the enterprise geodatabase.

Allow editors to createprivate versionsfor making isolated edits.

Implement a workflow for reconciling and posting edits after review.

ï‚· References:

Esri Documentation: Traditional Versioning.

Versioned Editing Best Practices: Guidelines for isolating and reviewing edits.

ï‚· Why the Correct Answer is B:Traditional versioning meets all requirements: it isolates edits, allows for review before posting, and reduces the number of RDBMS users through centralized version management. Branch versioning is web-centric and lacks the structured review process, while nonversioned editing does not support isolation or versioning.

Scenario Overview:

The organization hasArcGIS Enterpriseand requires versioned editing.

The project mandates tracking which user deleted a feature from thedefault version.

Why Branch Versioned Editing?

Branch versioningsupports versioned editing workflows and integrates seamlessly witheditor tracking, including operations like tracking who deleted a feature.

It is ideal for web-based workflows inArcGIS Enterpriseand allows for direct interaction with feature services.

The default version remains accessible for analysis while enabling the organization to track user edits, including feature deletions.(ArcGIS Documentation: Branch Versioning)

Alternative Options:

Option B: Traditional versioned editingsupports versioned workflows but does not inherently track who deletes features unless additional workflows are implemented (e.g., custom fields or triggers).

Option C: Nonversioned editingdoes not support versioning workflows or user tracking.

Thus,branch versioned editingis the best workflow to support versioned editing while tracking deleted features.

Scenario Overview:

Editors reconcile and post daily, but read-only versions created for analysis are not reconciled or posted.

The geodatabase is compressed nightly, but performance continues to degrade.

Cause of the Problem:

Unreconciled versions, including read-only ones, persist in the state tree, preventing the geodatabase compression from fully collapsing unused states.

Over time, this results in a bloated state tree and worsened performance.

Solution:

Reconciling and posting the read-only versions ensures that the state tree is cleared of unnecessary versions, enabling compression to collapse the database to its optimal state.

(ArcGIS Documentation: Reconcile and Post)

Alternative Options:

Option B: Creating a database view provides a read-only representation of data but does not address the underlying issue of unresolved states in the state tree.

Option C: Disabling editor tracking is unrelated to state tree performance issues and has no impact on the reconciliation or compression processes.

Therefore, reconciling the read-only versions will significantly improve performance.

When a database view is registered with the geodatabase, the geometry type, spatial reference, and row ID field are added to the geodatabase system tables. This enables ArcGIS to recognize and use the view effectively.

1. What Happens During Registration?

System Table Updates: Registering the view adds metadata to the geodatabase system tables, including:

The geometry type (e.g., point, polygon).

The spatial reference (coordinate system).

The row ID field used to uniquely identify rows.

This metadata ensures that the database view is treated as a spatial dataset within ArcGIS.

2. Benefits of Registration:

Spatial Functionality: Registered views can be used in ArcGIS for visualization, analysis, and querying.

Geodatabase Tools Compatibility: Tools like attribute editing and symbology work seamlessly with registered views.

3. Why Not Other Options?

Viewers Can Modify Which Fields Are Visible for the Database View:

The visibility of fields in a database view is determined by the SQL query used to create the view, not by the registration process.

The Registration Process Allows the Database Contents View to Be Edited Using ArcGIS Tools:

Registered views remain read-only in ArcGIS, regardless of registration. Registration does not allow editing the view’s contents.

Steps to Register a Database View with the Geodatabase:

OpenArcGIS Proand connect to the database.

Use theRegister With Geodatabasetool.

Specify the database view to be registered.

Confirm that the geometry type, spatial reference, and row ID field are properly recognized.

References from Esri Documentation and Learning Resources:

Registering Views with the Geodatabase

Geodatabase Metadata

Conclusion:

Registering a database view with the geodatabase ensures that thegeometry type, spatial reference, and row ID field are added to the system tables for reference, enabling ArcGIS to treat the view as a spatial dataset.

Scenario Overview:

The organization has a web service thatmust always be available.

The service reads data from afeature classin an enterprise geodatabase.

The GIS administrator needs to update theschemaof the feature class.

Why Disable Schema Locking?

By default, ArcGIS services enforceschema lockingto ensure data consistency while the service is active. This prevents any modifications to the feature class schema (e.g., adding fields, altering attributes) while the service is running.

Disabling schema locking allows schema updates to occur without disrupting the service's availability.(ArcGIS Documentation: Schema Locking)

Steps to Disable Schema Locking:

Access the ArcGIS Server Manager.

Locate the web service and open itsservice properties.

In the advanced settings, disable theschema lockingoption.

Perform the required schema updates (e.g., adding fields or modifying the feature class).

Re-enable schema locking if necessary for normal operation.

Alternative Options:

Option B: Run the Alter Field geoprocessing tool

This tool modifies fields but cannot execute schema changes while schema locks are active.

Option C: Delete the spatial index

Deleting the spatial index is unrelated to schema changes and could degrade query performance.

Thus, the correct workflow is todisable schema locking on the serviceto allow schema changes without disrupting the web service.

Scenario Overview:

The GIS data administrator is experiencinglagwhen changing the parameter value of adefinition query.

Definition queries dynamically filter data based on attribute values. Slow performance often indicates inefficient attribute searches.

Solution: Add Attribute Index

Anattribute indexallows the database to quickly locate rows based on values in the indexed column, significantly improving query performance.

When definition queries rely on non-indexed fields, the database must scan the entire dataset to filter records, leading to noticeable delays.

By creating an attribute index on the fields used in the definition query, the database can optimize filtering, reducing lag.(ArcGIS Documentation: Attribute Indexes)

Steps to Add Attribute Index:

In ArcGIS Pro, open theAttribute Indexestool.

Select thefeature class or tableused in the definition query.

Specify the field(s) that the definition query is based on.

ClickRunto create the index.

Alternative Options:

Option B: Add Spatial Index

Spatial indexes optimize spatial queries (e.g., finding features within an area). This does not address attribute-based definition query lag.

Option C: Recalculate Extent

Recalculating the extent corrects boundary discrepancies in spatial datasets but has no impact on attribute query performance.

Thus, adding anattribute indexis the correct action to resolve lag in definition queries.

ï‚· Understanding the Scenario:

The organization needs to make 10 million features accessible in ArcGIS Online.

The dataset must remain synchronized with the enterprise geodatabase to ensure it is up to date.

ï‚· Replication Options Overview:

Distributed Collaboration:Distributed collaboration enables sharing data across ArcGIS Enterprise and ArcGIS Online. However, it is not ideal for managing large datasets with frequent updates like this scenario.

Geodatabase Replication:

Designed for synchronizing data between enterprise geodatabases.

Supports one-way, two-way, or checkout replication to ensure updates are consistently transferred.

The best option for synchronizing a large dataset like this with ArcGIS Online via publishing workflows.

Delete and Append:This approach would involve overwriting the dataset frequently, which is inefficient and unsuitable for a dataset with 10 million features requiring frequent updates.

ï‚· Steps to Implement Geodatabase Replication:

Useone-way geodatabase replicationto replicate the dataset from the enterprise geodatabase to a secondary geodatabase.

Publish the replicated data to ArcGIS Online.

Set up periodic synchronization to ensure the ArcGIS Online data reflects updates from the enterprise geodatabase.

ï‚· References:

Esri Documentation: Geodatabase Replication.

Best Practices for Large Datasets in ArcGIS Online: Sharing Large Datasets.

ï‚· Why the Correct Answer is B:Geodatabase replication is explicitly designed for synchronizing large datasets between geodatabases, ensuring data consistency and compatibility with ArcGIS Online.