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First-hand SAP C_SAC_2501 Mock Test - C_SAC_2501 SAP Certified Associate - Data Analyst - SAP Analytics Cloud

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SAP C_SAC_2501 Exam Syllabus Topics:

Topic
Details

Topic 1

Story Design: This section of the exam measures the skills of Data Analysts and covers story design concepts, accounting for The focus on creating effective visualizations and narratives using data.

Topic 2

Connections and Data Preparation: This section targets Data Engineers and covers connections and data preparation, The focus is on connecting to data sources and preparing data for visualization.

Topic 3

Performance, Troubleshooting, and Security Management: This section measures the skills of IT Administrators and covers performance, troubleshooting, and security management, making up less than 10% of the exam. It focuses on optimizing performance, resolving issues, and ensuring data security in data visualization environments.

Topic 4

Planning: This section evaluates the expertise of Business Intelligence Developers. The focus is on planning and strategic aspects of data visualization projects.

Topic 5

Data Modeling, Analysis, and Integration: This section assesses the skills of Data Architects and covers data modeling, analysis, and integration.

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SAP Certified Associate - Data Analyst - SAP Analytics Cloud Sample Questions (Q35-Q40):

NEW QUESTION # 35
What type of predictive scenario can write back to a planning model?

A. Value driver tree
B. Regression
C. Classification
D. Time series forecast

Answer: D

Explanation:
In SAP Analytics Cloud, a Time Series Forecast predictive scenario can write back to a planning model. Time Series Forecasting leverages historical data to predict future values over a specified time horizon, using statistical or machine learning methods. This feature is particularly useful in planning and forecasting processes, where future values are predicted based on past trends and seasonality. The ability to write these forecasts back into a planning model allows for the integration of predictive insights into the planning process, enhancing decision-making and strategic planning.
Reference:
SAP Analytics Cloud Help Documentation: Predictive Scenarios and Planning SAP Analytics Cloud User Guide: Time Series Forecasting in Planning Models

NEW QUESTION # 36
How can you help a user enter data faster in a planning story? Note: There are 2 correct answers to this question.

A. Set Size Limits for Planning Performance in the model.
B. Enable Optimize Recommended Planning Area in the model.
C. Enable unbooked data in the story.
D. Select fluid data entry mode in the story.

Answer: B,D

NEW QUESTION # 37
What are the available connection types in SAP Analytics Cloud? Note: There are 2 correct answers to this question.

A. Live
B. Cloud
C. On-premise
D. Import

Answer: A,D

Explanation:
SAP Analytics Cloud supports two primary types of data connections: Live Data Connection and Import Data Connection. Live Data Connection establishes a direct link to the data source, allowing real-time data access without replicating the data into SAP Analytics Cloud. This is ideal for scenarios where up-to-the-minute data is crucial, and data volume is large. On the other hand, Import Data Connection involves copying data from the source into SAP Analytics Cloud, which is suitable for scenarios where data doesn't change frequently, or there's a need for data transformation and enrichment within SAP Analytics Cloud.
Reference:
SAP Analytics Cloud Help Documentation: Data Connections Overview
SAP Analytics Cloud User Guide: Live Data vs. Import Data Scenarios

NEW QUESTION # 38
Your embedded dataset in SAP Analytics Cloud has columns for Country, Region, City, and Customer Name. You want to aggregate measures for these columns as a single column. What can you do?

A. Create a level-based hierarchy in the dataset.
B. Convert the embedded dataset to a model.
C. Create a parent-child hierarchy in the dataset.
D. Create a group that includes the dimensions.

Answer: A

Explanation:
To aggregate measures for columns such as Country, Region, City, and Customer Name as a single column in an embedded dataset, creating a level-based hierarchy is the most effective approach. This type of hierarchy allows you to define a multi-level structure that represents the logical relationship between different geographical entities and customer names. By doing so, you can easily perform aggregations and analyze data at various levels of detail, from the broadest level (e.g., Country) down to the most specific one (e.g., Customer Name).
Reference:
SAP Analytics Cloud Help Documentation: Creating Hierarchies in Models
SAP Analytics Cloud User Guide: Data Modeling and Hierarchies

NEW QUESTION # 39
Which calculation types include dynamic date options? Note: There are 2 correct answers to this Question.

A. Difference From
B. Aggregation
C. Restricted Measure
D. Date Difference

Answer: A,C

NEW QUESTION # 40
......

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Frederick Martin Frederick Martin

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