Description

  • The “SAP Certified Application Professional – SAP Predictive Analytics 2.5” certification exam verifies that the candidate possesses proven skills and advanced knowledge in implementing and managing projects using SAP Predictive Analytics. This exam will measure the candidate’s knowledge of SAP Predictive Analytics end-to-end acquired as an key contributor in Predictive Analytics Project Teams with questions around Business Understanding, Data Acquisition and Preparation, Feature Engineering and Reduction, Visualization, Modeling, Model Accuracy and Integration. The certification verifies candidates expertise in both Automated Analytics and Expert Analytics as well as knowledge about SAP HANA (APL, PAL, and R) and HCP Services. It is expected that the candidate has gained sufficient experience through practical job experience to do well in the exam. Practical work experience will be tested on the exam with questions around use-cases including model management.

Notes

  • To ensure success, SAP recommends combining education courses and hands-on experience to prepare for your certification exam as questions will test your ability to apply the knowledge you have gained in training.
  • You are not allowed to use any reference materials during the certification test (no access to online documentation or to any SAP system).
  • Some language versions may not be available at Pearson VUE. Please check the available language versions >here.

Topic Areas

Please see below the list of topics that may be covered within this certification and the courses that cover them. Its accuracy does not constitute a legitimate claim; SAP reserves the right to update the exam content (topics, items, weighting) at any time.

Modelling – Expert Analytics > 12%

Explain the various algorithms like classification, regression, clustering, time series, etc. on different platforms e.g. HANA and Hadoop, API and scripting as well as R, custom components and data writers.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-59229-915-7
  • PA User Guides

Modelling – Automated Analytics > 12%

Explain the various functions like classification, regression, clustering, time series, social, recommendations, association rules on different platforms e.g. HANA and Hadoop, API and scripting as well as how to improve predictive power and confidence.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-4932-1326-9
  • PA User Guides

Model Integration and Consumption 8% – 12%

Explain how to export components or models e.g. to HANA, perform automated model consumption, and explain HCP services and API integration.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • HCP Predictive Services
  • HANA R Integration

Model Evaluation 8% – 12%

Explain statistical metrics, cluster, and category significance charts, how to detect leakers and unstable variables, and interpretation of a scorecard.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

Scenarios and Use Cases 8% – 12%

In given scenarios, describe requirements of data types for specific algorithms and explain why they are used

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-59229-915-7

Feature Engineering and Reduction 8% – 12%

Describe the usage of data manager, variable encoding, conversion of data types, new variables, composite position variables, normalization and data type conversion in EA, and the data manipulation in the prepare room.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

Data Preparation 8% – 12%

Perform data preparation like data sampling, outlier handling and detection, filtering and handling of missing values.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-59229-915-7

Modelling – Embedded < 8%

Explain how to use Automated Predictive Library (APL) and Predictive Analytics Library (PAL).

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-59229-915-7
  • PAL Reference Guide
  • Application Function Library
  • SAP HANA Academy

Data Acquisition < 8%

Describe and explain how to acquire and merge data, work with multiple files and describe the precreated visualizations for data nodes.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

Model Management and Monitoring < 8%

Explain model management like detecting model deviations, scheduling and retraining of a model as well as management of user roles and authorizations.

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

Business Understanding and Process Modelling < 8%

Describe the differences between traditional modeling  vs. automated analytics model building approach, explain Vapnik principles and overfitting. 

  • PAII10 (PREDICTIVE ANALYTICS)
  • PAII11 (PREDICTIVE ANALYTICS)
  • PAII12 (PREDICTIVE ANALYTICS)

—– OR —–

  • ISBN 978-1-59229-915-7