CDW110V CABOODLE DATA MODEL
FUNDAMENTALS EXAM STUDY GUIDE
2026 COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS || 100%
GUARANTEED PASS <RECENT
VERSION>
Section 1: Caboodle Core Concepts & Architecture
1. What is the primary purpose of the Caboodle Data Warehouse within the
Epic EHR ecosystem?
A) To serve as the real-time transactional database for patient care.
B) To provide a centralized, optimized repository for reporting and analytics.
C) To store all unstructured data like clinician notes and PDFs.
Explanation: Caboodle is a data warehouse designed for analytical processing, not
for real-time patient transactions (which is Clarity's role). It is structured for fast
and efficient querying for reports and dashboards.
2. How does the data typically flow into the Caboodle data warehouse?
A) Directly from the Chronicles production database.
B) From the Clarity database via ETL (Extract, Transform, Load) processes.
C) Through real-time HL7 feeds from ancillary systems.
Explanation: Clarity is the operational data store (ODS) extracted from
Chronicles. Caboodle is then populated from Clarity using ETL jobs that transform
the data into its dimensional model.
3. What is the foundational data modeling structure used in Caboodle?
A) Entity-Relationship (ER) Model
B) Dimensional Modeling (Star Schema)
C) Object-Oriented Model
Explanation: Caboodle uses a dimensional model organized into Fact and
,Dimension tables, forming star schemas to optimize query performance for
analytics.
4. The Caboodle data model is pre-built and supplied by Epic. Can it be
modified?
A) Yes, customers can directly add tables and columns to the core model.
B) No, the core model is fixed, but customers can create custom tables and ETLs in
their own schema.
C) Modifications are only allowed by Epic engineers during implementation.
Explanation: To ensure upgradeability and support, the core Caboodle model is
locked. However, Epic provides mechanisms for customers to build their own
custom data assets (CABI tables) alongside it.
5. What is the primary advantage of using a dimensional model in Caboodle?
A) It minimizes data storage space.
B) It simplifies query writing and improves performance for reporting.
C) It enforces real-time data consistency.
Explanation: The star schema's simplified structure, with a central fact table
surrounded by dimensions, makes it intuitive for report builders to create queries
and allows the database engine to perform rapid aggregations.
Section 2: Fact Tables
6. Fact tables in a dimensional model primarily contain:
A) Descriptive textual information about business entities (e.g., Patient Name,
Department Name).
B) Numerical measurements and metrics from business processes (e.g., Procedure
Count, Revenue).
C) The definitions and hierarchies for reporting categories.
Explanation: Fact tables are the "what" you are measuring. They are full of foreign
keys to dimensions and numeric facts like counts, amounts, and durations.
7. Which of the following is most likely a fact table in Caboodle?
A) D_PATIENT
B) F_PATIENT_ENCOUNTER
C) H_DEPARTMENT
, Explanation: The "F_" prefix in Caboodle typically denotes a Fact table.
F_PATIENT_ENCOUNTER would contain metrics about patient visits.
8. What are the two main types of facts found in fact tables?
A) Primary Facts and Secondary Facts
B) Additive Facts and Semi-Additive Facts
C) Static Facts and Dynamic Facts
Explanation: Additive facts (e.g., procedure count) can be summed across all
dimensions. Semi-additive facts (e.g., account balance) can only be summed across
some dimensions (like patients) but not others (like time).
9. A "factless" fact table is used to record:
A) Events that have no measurable outcomes.
B) The occurrence of an event or the relationship between dimensions.
C) Facts that have been deleted from the source system.
Explanation: A factless fact table has no measurable facts, only foreign keys. It's
used to track events like student attendance (the event happened) or coverage (a
relationship between patient and insurance).
10. The grain of a fact table is defined as:
A) The level of detail at which data is stored in the table.
B) the number of rows in the table.
C) The data type of its primary key.
Explanation: The grain is the fundamental, atomic level of data in the fact table
(e.g., one row per encounter, one row per clinical event). Defining the grain is a
critical first step in designing a fact table.
Section 3: Dimension Tables
11. Dimension tables in a dimensional model primarily contain:
A) Numerical measurements and metrics.
B) Descriptive attributes and context used for filtering and grouping.
C) The ETL job logs and metadata.
Explanation: Dimensions provide the "who, what, where, when, why" context for
the facts. They are used in the WHERE and GROUP BY clauses of queries.
FUNDAMENTALS EXAM STUDY GUIDE
2026 COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS || 100%
GUARANTEED PASS <RECENT
VERSION>
Section 1: Caboodle Core Concepts & Architecture
1. What is the primary purpose of the Caboodle Data Warehouse within the
Epic EHR ecosystem?
A) To serve as the real-time transactional database for patient care.
B) To provide a centralized, optimized repository for reporting and analytics.
C) To store all unstructured data like clinician notes and PDFs.
Explanation: Caboodle is a data warehouse designed for analytical processing, not
for real-time patient transactions (which is Clarity's role). It is structured for fast
and efficient querying for reports and dashboards.
2. How does the data typically flow into the Caboodle data warehouse?
A) Directly from the Chronicles production database.
B) From the Clarity database via ETL (Extract, Transform, Load) processes.
C) Through real-time HL7 feeds from ancillary systems.
Explanation: Clarity is the operational data store (ODS) extracted from
Chronicles. Caboodle is then populated from Clarity using ETL jobs that transform
the data into its dimensional model.
3. What is the foundational data modeling structure used in Caboodle?
A) Entity-Relationship (ER) Model
B) Dimensional Modeling (Star Schema)
C) Object-Oriented Model
Explanation: Caboodle uses a dimensional model organized into Fact and
,Dimension tables, forming star schemas to optimize query performance for
analytics.
4. The Caboodle data model is pre-built and supplied by Epic. Can it be
modified?
A) Yes, customers can directly add tables and columns to the core model.
B) No, the core model is fixed, but customers can create custom tables and ETLs in
their own schema.
C) Modifications are only allowed by Epic engineers during implementation.
Explanation: To ensure upgradeability and support, the core Caboodle model is
locked. However, Epic provides mechanisms for customers to build their own
custom data assets (CABI tables) alongside it.
5. What is the primary advantage of using a dimensional model in Caboodle?
A) It minimizes data storage space.
B) It simplifies query writing and improves performance for reporting.
C) It enforces real-time data consistency.
Explanation: The star schema's simplified structure, with a central fact table
surrounded by dimensions, makes it intuitive for report builders to create queries
and allows the database engine to perform rapid aggregations.
Section 2: Fact Tables
6. Fact tables in a dimensional model primarily contain:
A) Descriptive textual information about business entities (e.g., Patient Name,
Department Name).
B) Numerical measurements and metrics from business processes (e.g., Procedure
Count, Revenue).
C) The definitions and hierarchies for reporting categories.
Explanation: Fact tables are the "what" you are measuring. They are full of foreign
keys to dimensions and numeric facts like counts, amounts, and durations.
7. Which of the following is most likely a fact table in Caboodle?
A) D_PATIENT
B) F_PATIENT_ENCOUNTER
C) H_DEPARTMENT
, Explanation: The "F_" prefix in Caboodle typically denotes a Fact table.
F_PATIENT_ENCOUNTER would contain metrics about patient visits.
8. What are the two main types of facts found in fact tables?
A) Primary Facts and Secondary Facts
B) Additive Facts and Semi-Additive Facts
C) Static Facts and Dynamic Facts
Explanation: Additive facts (e.g., procedure count) can be summed across all
dimensions. Semi-additive facts (e.g., account balance) can only be summed across
some dimensions (like patients) but not others (like time).
9. A "factless" fact table is used to record:
A) Events that have no measurable outcomes.
B) The occurrence of an event or the relationship between dimensions.
C) Facts that have been deleted from the source system.
Explanation: A factless fact table has no measurable facts, only foreign keys. It's
used to track events like student attendance (the event happened) or coverage (a
relationship between patient and insurance).
10. The grain of a fact table is defined as:
A) The level of detail at which data is stored in the table.
B) the number of rows in the table.
C) The data type of its primary key.
Explanation: The grain is the fundamental, atomic level of data in the fact table
(e.g., one row per encounter, one row per clinical event). Defining the grain is a
critical first step in designing a fact table.
Section 3: Dimension Tables
11. Dimension tables in a dimensional model primarily contain:
A) Numerical measurements and metrics.
B) Descriptive attributes and context used for filtering and grouping.
C) The ETL job logs and metadata.
Explanation: Dimensions provide the "who, what, where, when, why" context for
the facts. They are used in the WHERE and GROUP BY clauses of queries.
