SAP c-bcbdc-2505 practice test

Answer:

B, D

Answer:

C

Explanation:
The primary storage type for the object store within the SAP Business Data Cloud (BDC) architecture
is SAP HANA data lake files. SAP BDC is designed to handle vast amounts of diverse data, including
semi-structured and unstructured data, which is efficiently stored in a data lake. The SAP HANA data
lake, specifically its file storage component, provides a highly scalable and cost-effective solution for
retaining raw, historical, and detailed data. This contrasts with traditional relational databases (like
SAP HANA extended tables) or data warehousing constructs (like BW/4HANA DataStore objects or
InfoObjects), which are optimized for structured, aggregated data and specific query patterns. The
object store's reliance on data lake files in BDC underscores its capability to manage enterprise-wide
data regardless of its structure, making it suitable for a wide range of analytical workloads, including
those involving machine learning and advanced analytics where raw data access is crucial.

Answer:

C, D

Explanation:
The SAP Business Data Cloud (BDC) Cockpit serves as the central administrative and operational
interface for managing the BDC environment. Among its core functionalities, it directly supports the
ability to configure SAP Business Data Cloud. This includes setting up connections, managing spaces,
configuring system parameters, and generally overseeing the platform's infrastructure. It provides
administrators with the necessary tools to tailor the BDC environment to specific organizational
needs. Additionally, the cockpit is instrumental in allowing users to discover and activate data
products. Data products are pre-built, semantically rich data assets that encapsulate business logic
and data from various sources, offered within the BDC ecosystem. The cockpit acts as a marketplace
or catalog where users can find relevant data products, understand their content, and activate them
for use in their analytics and applications. While "Enhance Analytic Models" is done in tools like SAP
Datasphere's Data Builder and debugging authorization issues might involve various tools, direct
configuration and data product management are key features of the BDC Cockpit.

Answer:

C

Explanation:
In the context of SAP Business Data Cloud (BDC), SAP Datasphere plays a pivotal role primarily to
provide analytic models for intelligent applications. SAP Datasphere acts as the unified data fabric
and central data layer within the BDC architecture. It is where data from various sources is
integrated, harmonized, and semantically enriched. The analytical models, which are the foundation
for reporting, dashboards, and machine learning initiatives within intelligent applications, are built
and managed within SAP Datasphere. These models transform raw, integrated data into business-
ready information, providing the necessary structure and context for consumption by SAP Analytics
Cloud and other intelligent applications. While data products are defined using artifacts within
Datasphere, and the overall system landscape is maintained through the BDC Cockpit, the core
purpose of Datasphere in this ecosystem is its capability to deliver robust, high-quality analytical
models to drive business insights for intelligent applications.

Answer:

C

Explanation:
The Foundation Services component of SAP Business Data Cloud (BDC) is responsible for
orchestrating the fundamental processes of data preparation and productization. Specifically, a key
operation implemented by Foundation Services is data transformation and enrichment to generate a
data product. Foundation Services takes raw data ingested from various business applications and
applies necessary transformations, cleanses it, and enriches it with additional context or calculated
attributes. This process is crucial for creating high-quality, consumable data products, which are
curated and semantically rich datasets designed for specific business use cases. While machine
learning algorithms are executed by Intelligent Applications (which consume these data products),
and analytic models are built in SAP Datasphere (which is part of the BDC ecosystem), Foundation
Services focuses on the foundational work of preparing and productizing the data itself, ensuring it's
ready for advanced analytics and consumption.

Answer:

A, B

Explanation:
The out-of-the-box reporting capabilities with intelligent applications in SAP Business Data Cloud
(BDC) are designed to streamline the analytical process and deliver immediate value. Two significant
features include automated data provisioning from business application to dashboard. This means
that intelligent applications handle the end-to-end flow of data, from its source in operational
systems, through processing in BDC, and finally to visualization in dashboards, with minimal manual
intervention. This automation ensures timely and consistent data delivery for reporting. Additionally,
these intelligent applications leverage services for transforming and enriching data. As part of the
pre-built logic within these applications, data is automatically transformed (e.g., aggregated, filtered)
and enriched (e.g., adding calculated KPIs, combining with master data) to make it immediately
suitable for reporting and analysis. This reduces the need for manual data manipulation by users,
providing ready-to-consume insights.

Answer:

B, D

Explanation:
B . ABAP CDS view →
ABAP CDS views in SAP S/4HANA or SAP BW systems are supported sources.
Replication Flows can pull data directly from CDS views into Datasphere targets.
This is a standard use case for SAP-to-Datasphere replication.
D . MS Azure SQL table →
Azure SQL tables are supported as cloud sources in Replication Flows.
You can replicate these tables into SAP Datasphere targets.

Answer:

B, D

Explanation:
In SAP Datasphere, there are several ways to create a local table with a custom name, providing
flexibility for data management. Two common methods are by importing a CSV file and by adding an
output of a data flow. When you import a CSV file, Datasphere allows you to specify a custom name
for the new local table that will store the imported data. This is a quick and straightforward way to
bring external, flat-file data into Datasphere. Secondly, a data flow in Datasphere allows you to define
a sequence of operations (e.g., transformations, aggregations) and write the processed data to a
target. When configuring the output of a data flow, you can specify a new local table and provide it
with a custom name. This method is ideal for creating structured tables as a result of complex data
integration or transformation processes. These options ensure that users can create and name tables
according to their specific data modeling and organizational requirements.

Answer:

C, D, E

Explanation:
The remote table feature in SAP Datasphere offers significant flexibility in how data from external
sources is consumed and managed. Firstly, data can be accessed virtually by remote access to the
source system (E). This means Datasphere does not store a copy of the data; instead, it queries the
source system in real-time when the data is requested. This ensures that users always work with the
freshest data. Secondly, data can be persisted in SAP Datasphere by creating a snapshot (copy of
data) (C). This allows users to explicitly load a copy of the remote table's data into Datasphere at a
specific point in time, useful for performance or offline analysis. Lastly, data can be persisted by using
real-time replication (D). For certain source systems and configurations, Datasphere supports
continuous, real-time replication, ensuring that changes in the source system are immediately
reflected in the persisted copy within Datasphere. Option A is incorrect as the access mode cannot be
arbitrarily switched, and option B refers to data flow capabilities, not inherent remote table access
options.

Answer:

C

Explanation:
C . Replication Flow →
Purpose: To replicate/move data from Datasphere to outbound targets such as:
SAP HANA Cloud
Data Lakes
External databases
This is the only flow type that supports outbound replication from local tables.
Exactly matches the question requirement.