Which programming language is used for scripting in an SAP Analytics Cloud story?
D
Explanation:
JavaScript is the programming language utilized for scripting within an SAP Analytics Cloud (SAC)
story. While SAC offers various functionalities through its intuitive user interface, scripting with
JavaScript provides advanced capabilities for customizing the behavior and interactivity of a story.
This allows developers and power users to create highly tailored analytical applications and
dashboards that go beyond standard features. For instance, JavaScript can be used to dynamically
change chart properties, implement complex filtering logic, trigger data actions, or integrate with
external services. Unlike analytic applications, which typically offer more extensive scripting options,
storytelling in SAC focuses on enabling business users to create interactive reports with a degree of
customization through embedded scripts. The scripts are executed by the web browser, leveraging its
built-in JavaScript execution engine, ensuring a flexible and widely understood development
environment for enhancing story functionality.
Which SAP Analytics Cloud feature uses natural language processing?
B
Explanation:
The "Just Ask" feature in SAP Analytics Cloud (SAC) is a prime example of its integration with natural
language processing (NLP). This innovative AI-powered capability allows users to interact with their
data by simply typing questions in plain, everyday language, rather than needing to navigate
complex menus or understand underlying data structures. For instance, a user might type "Show me
sales by region for the last quarter," and "Just Ask" will interpret this query, identify relevant
dimensions and measures, and automatically generate an appropriate visualization or insight. This
significantly democratizes data analysis, making it accessible to a wider audience, including business
users who may not have extensive technical skills. By leveraging NLP, "Just Ask" bridges the gap
between human language and data queries, transforming how users discover and consume insights
within SAC, ultimately accelerating decision-making.
What features are supported by the SAP Analytics Cloud data analyzer? Note: There are 3 correct
answers to this question.
A, B, C
Explanation:
The SAP Analytics Cloud Data Analyzer is designed for ad-hoc data exploration and analysis,
providing a focused environment for users to quickly derive insights. Among its key supported
features are calculated measures, which allow users to create new metrics on the fly based on
existing data, enabling deeper analysis without modifying the underlying model. Input controls are
also supported, providing interactive filtering capabilities that allow users to dynamically adjust the
data displayed based on specific criteria, enhancing the flexibility of their analysis. Furthermore,
conditional formatting is a valuable feature that enables users to apply visual styling (e.g., colors,
icons) to data points based on defined rules, making it easier to identify trends, outliers, or specific
conditions at a glance. While charts and linked dimensions are integral to full stories, the Data
Analyzer's strength lies in its immediate, flexible analytical capabilities for a single data source.
In SAP Analytics Cloud, you have a story based on an import model. The transactional data in the
model's data source changes. How can you update the data in the model?
D
Explanation:
When an SAP Analytics Cloud (SAC) story is based on an import model, the data is physically copied
and stored within SAC. Therefore, simply refreshing the story (option A) will only update the
visualization with the data already in the model and will not pull new data from the source. Similarly,
"Allow model import" (option B) isn't a direct action for updating data, but rather a prerequisite for
the import process itself. "Refresh the data source" (option C) is not an action performed within SAC
for an import model. To update the data in the model when the transactional data in its source
changes, you must schedule the import (option D) or manually re-run the import process. This
process re-fetches the latest data from the original source system and updates the SAC import
model, ensuring your story reflects the most current information. This scheduling can be set up to
occur at regular intervals, keeping the model synchronized with the source data.
For a model in SAP Analytics Cloud you are using a live connection. Where is the data stored?
C
Explanation:
When an SAP Analytics Cloud (SAC) model utilizes a live connection, the data is not stored within SAP
Analytics Cloud itself. Instead, the data resides entirely in the source system. This means that SAC
directly queries the data from the connected system (e.g., SAP HANA, SAP BW, SAP S/4HANA, or SAP
Datasphere) in real-time every time a user interacts with the story or application. Only metadata,
such as dimension definitions and measure aggregations, is stored in SAC. This approach offers
several significant advantages: it ensures that users always work with the most current data,
eliminates the need for data replication, and often addresses data privacy and security concerns by
keeping sensitive data within the customer's secure landscape. The "live" nature means that any
changes in the source system are immediately reflected in SAC.
Which automatically created dimension type can you delete from an SAP Analytics Cloud analytic
data model?
A
Explanation:
In an SAP Analytics Cloud (SAC) analytic data model, you typically have a degree of flexibility in
managing dimensions. Among the automatically created dimension types, the Generic dimension
can often be deleted if it's not relevant or desired for your analysis. Generic dimensions are often
generated by the system based on identified data patterns but might not always align with specific
business requirements or be redundant. In contrast, Date, Version, and Organization dimensions are
fundamental and often system-critical, especially for planning models (Version, Organization) or
time-based analysis (Date). These core dimensions are usually not freely deletable or are required by
the system for specific functionalities. Therefore, for tailoring your analytic model to specific business
needs, the ability to remove generic dimensions provides greater control and simplification.
In an SAP Analytics Cloud planning data model, which dimensions are included by default? Note:
There are 2 correct answers to this question.
B, D
Explanation:
When creating a planning data model in SAP Analytics Cloud (SAC), certain dimensions are included
by default to facilitate common planning scenarios. The two key dimensions automatically present
are Version and Date. The Version dimension is crucial for distinguishing between different planning
scenarios, such as "Actual," "Budget," "Forecast," or "Plan 2025," allowing users to compare and
manage various iterations of their planning data. The Date dimension, on the other hand, is essential
for time-based planning and analysis, enabling data entry, aggregation, and reporting across different
time granularities like years, quarters, months, or days. These default dimensions provide a robust
framework for financial and operational planning, serving as foundational elements around which
planning activities are structured, and ensuring consistency and comparability across different
planning versions and time periods.
What is required to use version management in an SAP Analytics Cloud story?
D
Explanation:
To leverage version management capabilities within an SAP Analytics Cloud (SAC) story, it is a
fundamental requirement that the story is built on a planning model. Version management is a core
feature specifically designed for planning functionalities. It enables users to create, manage, and
compare different scenarios or iterations of data, such as "Actual," "Budget," "Forecast," or various
planning versions. This is critical for budgeting, forecasting, and what-if analysis, allowing planners to
work on different data sets concurrently and track changes over time. While analytic models are
used for general reporting and analysis, they do not inherently support the robust version
management features that are integral to planning processes. Therefore, if you intend to utilize
version management to compare different data scenarios or manage planning cycles, your SAC story
must be connected to a planning model.
What source system can you connect to with an SAP Analytics Cloud live connection that is provided
by SAP BDC?
B
Explanation:
Within the context of SAP Business Data Cloud (BDC), the primary and most central source system for
an SAP Analytics Cloud (SAC) live connection is SAP Datasphere. SAP Datasphere serves as the
comprehensive data foundation for SAP's business data fabric, integrating data from various SAP and
non-SAP sources, and providing a harmonized, semantically rich data layer. SAP BDC is built upon and
extends the capabilities of SAP Datasphere, making it the strategic hub for analytics and data
management. Therefore, to ensure data consistency, governance, and real-time access to the
integrated and harmonized business data managed within the BDC ecosystem, SAP Datasphere is the
recommended and primary live connection source for SAC. While SAC can connect to other SAP
systems like S/4HANA or BW, within the specific architecture of SAP BDC, SAP Datasphere plays the
pivotal role as the integrated data platform.
Related to data management, what are some capabilities of SAP Business Data Cloud? Note: There
are 2 correct answers to this question.
B, D
Explanation:
SAP Business Data Cloud (BDC) offers significant capabilities in data management, primarily focusing
on creating a unified and actionable data foundation. Two key capabilities are to integrate and enrich
customer business data for different analytics use cases. BDC pulls data from various SAP and non-
SAP sources, allowing for consolidation and enhancement of this data to provide a comprehensive
view for analytical purposes. This includes applying business context and semantic richness.
Secondly, a critical capability is to harmonize customer business data across different Line of Business
applications. BDC addresses the challenge of disparate data silos by creating a consistent data model
and definitions across various operational systems (e.g., ERP, CRM, HR), ensuring that data is
understood and used uniformly across the enterprise. While BDC leverages hyperscaler
environments, "storing data" is a characteristic of its infrastructure, not a direct capability of data
management provided by BDC itself. Delegating integration is an operational choice, not a core
capability of the platform.
Which steps are executed when an SAP Business Data Cloud Intelligent Application is installed? Note:
There are 2 correct answers to this question.
B, D
What is the main storage type of the object store in SAP Business Data Cloud?
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.
Which of the following activities does SAP Business Data Cloud cockpit support? Note: There are 2
correct answers to this question.
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.
What is a purpose of SAP Datasphere in the context of SAP Business Data Cloud?
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.
Which operation is implemented by the Foundation Services of SAP Business Data Cloud?
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.