Answer:

C, D, E

Answer:

A, D, E

Explanation:
The Data Builder in SAP Datasphere is the primary environment for data modeling and
transformation activities. Within the Data Builder, users can create a variety of essential objects to
build their data landscape. Among the options provided, you can create Intelligent Lookups (A),
which are used for fuzzy matching and data cleansing operations to link disparate data sets. You can
also create Task Chains (D), which are crucial for orchestrating and automating sequences of data
integration and transformation processes, ensuring data pipelines run efficiently. Furthermore,
Replication Flows (E) are designed and managed within the Data Builder, allowing you to configure
and execute continuous or scheduled data replication from source systems into Datasphere. "Spaces"
(B) and "Connections" (C) are typically managed at a higher administrative level within the SAP
Datasphere tenant (e.g., in the System or Connection Management areas), not directly within the
Data Builder itself, which focuses on data content and logic.

Answer:

D

Explanation:
When faced with the challenge of combining data from different sources where the matching keys
(like "Product ID") are inconsistent or contain variations (e.g., "O" vs. "0"), the recommended artifact
in SAP Datasphere for such fuzzy or approximate matching scenarios is an Intelligent Lookup. An
Intelligent Lookup (D) leverages machine learning capabilities to identify and map records that are
semantically similar but not exact matches. Unlike standard joins in graphical views or SQL views
which require precise key matches, Intelligent Lookups can handle data quality issues, typos, and
variations, allowing you to successfully link disparate records that would otherwise be missed. This is
particularly valuable when integrating data from external systems or legacy sources where perfect
data standardization is not feasible, ensuring a more comprehensive and accurate combined dataset
for analysis.

Answer:

B

Explanation:
An SAP Datasphere analytic model is specifically designed for multi-dimensional analysis, and as
such, it requires a central entity that contains the measures (key figures) to be analyzed and links to
descriptive dimensions. Therefore, a View of semantic type Fact (B) is the most appropriate and
commonly used direct source for an analytic model. A "Fact" view typically represents transactional
data, containing measures (e.g., sales amount, quantity) and foreign keys that link to dimension
views (e.g., product, customer, date). While "Dimension" type entities (A) provide descriptive
attributes and are linked to the analytic model, they are not the direct source of the model itself.
Tables of semantic type Hierarchy (C) are used within dimensions, and remote tables of semantic
type Text (D) typically provide text descriptions for master data, not the core fact data for an analytic
model. The Fact view serves as the central point for an analytic model's measures and its
connections to all relevant dimensions.

Answer:

C,D

Explanation:
C . Create an SQL view with a JOIN operation →
SQL views in Datasphere allow you to write SQL code directly.
You can use JOIN in your SQL script to combine multiple artifacts (tables/views).
SELECT a.CustomerID, b.SalesAmount
FROM Customers a
JOIN Sales b ON a.CustomerID = b.CustomerID;
D . Create a graphical view, drag an artifact to the canvas, and the second one on top of the first one
→
In the Datasphere graphical modeler, when you drag the second artifact onto the first one, the
system automatically creates a Join node.
You can then define the join type (Inner, Left Outer, Right Outer, Full).
This is the drag-and-drop method for joins.

Answer:

A, D

Answer:

A

Explanation:
What do you use to write data from a local table in SAP Datasphere to an outbound target?

Answer:

A, C, D

Explanation:
SAP Datasphere task chains are powerful tools for orchestrating and automating sequences of
operations, making them ideal for managing complex data pipelines and recurring processes. One
key use case is to Create or Refresh View Persistency (A). If you have views for which you want to
persist the data (materialize them into tables) for performance or specific analytical needs, a task
chain can automate the scheduled recreation or refresh of these persistent views. Another common
use case is to Execute a Replication Flow and Transformation Flow in sequence (C). This allows you to
define a process where data is first replicated from a source system into Datasphere, and then
immediately followed by transformation steps to cleanse, enrich, or aggregate that data, ensuring a
fully automated end-to-end data preparation. Furthermore, task chains can be used to Run an Open
SQL Schema Procedure (D). This provides flexibility to integrate custom SQL logic or stored
procedures into an automated workflow, enabling advanced data manipulation or administrative
tasks. Uploading a CSV file (B) is typically a manual import action, and executing a data action for a
planning function (E) relates to planning models, not general Datasphere task chains.

Answer:

A, D

Explanation:
A database user in SAP Datasphere serves specific technical and security-related purposes that are
distinct from typical modeling activities within the Data Builder. One primary purpose is to directly
access the SAP HANA Cloud database of SAP Datasphere. For advanced scenarios, such as debugging,
executing complex SQL scripts directly, or integrating with specialized tools that require direct
database connectivity, a dedicated database user is essential. This access bypasses the higher-level
Datasphere modeling environment and interacts directly with the underlying SAP HANA Cloud
instance. Another crucial purpose is to provide a secure method for data exchange for 3rd party
tools. When external applications, reporting tools, or data integration platforms need to consume
data from or write data into SAP Datasphere's underlying database, a database user provides the
necessary authentication and authorization mechanism. This ensures that data exchange is secure
and controlled, adhering to defined permissions. Creating graphical views (B) is done via the
Datasphere UI with a Datasphere user, and accessing all schemas (C) would typically require broad
administrative privileges, which might be granted to specific database users, but the core purpose is
controlled access, not carte blanche.

Answer:

D

Explanation:
The "Validate Remote Tables" feature in the SAP Datasphere repository explorer is primarily used to
identify structure updates of the remote sources. When a remote table is created in Datasphere, it
establishes a metadata connection to a table or view in an external source system. Over time, the
structure of the source object (e.g., column additions, deletions, data type changes) might change.
The "Validate Remote Tables" function allows you to compare the metadata currently stored in
Datasphere for the remote table with the actual, current metadata in the source system. If
discrepancies are found, Datasphere can highlight these structural changes, prompting you to update
the remote table's definition within Datasphere to match the source. This ensures that views and
data flows built on these remote tables continue to function correctly and align with the underlying
source structure, preventing data access issues or incorrect data interpretations.

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.

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:

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:

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:

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.