Teradata Certification- Data Engineering Exam

Version Vantage 2.2 
and  17.05 
July 2020 
Data Engineering Exam Objectives 
1
 
TERADATA.COM 
 
 
Teradata Certification- Data Engineering Exam
 
 
Exam Objectives
 
 
The Data Engineering Exam covers the features and functionality of Vantage 2.2 
including the Advanced SQL Engine through release 17.05. 
 
This document now LINKS directly to the free web-based training course that supports 
each objective as well as to the full curriculum above. 
 
Manage and Optimize Data Solutions  – 16%
 
    
•  Given a scenario, identify the type of secondary or join indexes that should be created to 
improve performance of a class of queries. Additional training here.  
•  Identify the characteristics and usage of row and column partitioning, constraints, and 
dynamic partition elimination. 
•  Identify the processes for efficient database design (e.g., data profiling, data domains, 
data and join demographics, and access patterns.) Additional training here.  
•  Identify the characteristics and impacts of system defaults (e.g.,  currency, current date, 
timestamp, for a geographic region, etc.)  
•  Given an Explain plan, identify the processing that would be executed. 
•  Given a scenario about the requirements from a data scientist for new data features, 
identify the steps to integrate the features. 
•  Identify the considerations when creating or using an access layer. 
•  Identify the benefits, usage, and limitations of using QueryGrid and other approaches as 
part of a data access strategy. Additional training here.  
 
Transaction Processing and Lock Management – 8% 
•  Given a scenario, identify advanced locking types and implications (e.g.,  READ UNCOMMITTED 
(access lock), COMMITTED (load isolation), and SERIALIZABLE  (read lock).)   
•  Identify the characteristics of a transaction, request and statement. 
•  Given a scenario, identify the impact of session characteristics on request (e.g.,  error recovery 
implications such as dead-lock situations, ANSI vs BTET, min and max sessions, date format, default 
character set, etc.) 
    
SQL Concepts– 39% 
•  Identify the usage and characteristics of macros.  
•  Identify the  usage and characteristics of stored procedures. 
•  Identify the usage and characteristics of tables (e.g., permanent, error, and QUEUE) 
•  Identify the usage and characteristics of advanced data types (JSON, Period) 
•  Identify the usage and characteristics of column level attributes (e.g., FORMAT, DEFAULT, 
CASESPECIFIC, COMPRESS, etc.) Additional training here.  
•  Identify the usage and characteristics of basic data types (e.g., VARCHAR, CHAR, DATE, 
TIMESTAMP, LOB, etc.) 
•  Identify the usage and characteristics of table level and column level constraints (e.g., 
Check, Primary Key, References, and Unique) 

Version Vantage 2.2 
and  17.05 
July 2020 
Data Engineering Exam Objectives 
2
 
TERADATA.COM 
 
 
•  Identify the types, usage, characteristics, and implications of referential integrity (RI). 
•  Identify the usage and characteristics of primary index and NoPI tables.  
•  Identify the usage and characteristics of secondary indexes.  
•  Identify the usage and characteristics of join indexes.  
•  Identify the use cases of, performance, characteristics, and implications including 
maintenance issues of column or row partitioning.   
•  Identify usage and characteristics of views (e.g.,  nested, base (1:1), updateable, etc.)   
•  Identify the situations when global temporary tables or volatile temporary tables should be 
used and the limitations of their use. 
•  Identify usage and characteristics of the advanced regular expression functions 
(e.g.,  REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_SIMILAR, REGEXP_REPLACE, 
REGEXP_SPLIT_TO_TABLE.)  
•  Identify the usage and characteristics of derived tables.  
•  Identify the usage and characteristics of the advanced ANSI SQL:2011 Window aggregates 
including Group window, cumulative window, and moving window.  
•  Identify the usage and characteristics of JSON functions (e.g.,  compression.) 
•  Identify the usage and characteristics of common clauses, operators, and expressions 
(e.g.,  SAMPLE, RANDOM, TOP, CAST, etc.)  
•  Identify the usage and characteristics of Period data type functions (e.g.,  BEGIN, END, 
OVERLAPS, LDIFF, RDIFF, UNTIL_CHANGED/IS NOT UNTIL_CHANGED, 
IMMEDIATELY PRECEDES, and IMMEDIATELY SUCCEEDS, TD_NORMALIZE, etc.) 
•  Identify the characteristics and implications of using correlated subqueries. 
•  Identify the usage and characteristics of ANSI SQL:2011 Window aggregate functions 
(e.g.,  GROUP functions including QUALIFY, ROW_NUMBER, COUNT, MIN, MAX, AVG, 
RANK.)   
•  Identify the implications of nulls with inner and outer joins. 
•  Identify the usage and characteristics of scalar functions available in Teradata (Teradata 
and ANSI variants, including embedded services functions)  
•  Identify the characteristics and implications  of a scalar or a nonscalar subquery. 
•  Identify the function, characteristics and implications of joins and types of join (e.g.,  inner 
join, outer joins, cross join.) 
•  Identify the characteristics and  implications of subqueries in different parts (e.g.,  WHEN 
clauses, CASE statements, FROM clauses, etc.) of the SQL statement. 
•  Identify the characteristics and implications of MERGE including logging errors.  
•  Identify the characteristics and implications of INSERT, UPDATE, and DELETE statements. 
Data Integration Strategies – 13% 
•  Identify the characteristics, limitations, and usage of TPT LOAD, EXPORT,  UPDATE 
(including MLOADX) and STREAM operators. Additional training here.  
•  Given a TPT job, identify the additional considerations for error handling and job handling 
that should be used.  
•  Identify the characteristics, limitations, and usage of TPT DDL, ODBC, SQL INSERTER, 
and SQL SELECTOR operators. 
•  Given a scenario, identify the optimal load strategy (e.g.,  mini-batch, TPT LOAD / 
UPDATE, TPT STREAM, etc.)  
•  Identify the benefits, usage, and limitations of using access modules as part of a data 
access strategy. 
Solution Optimization – 13% 
•  Given a scenario, identify performance tuning processes and options to improve an 
existing application (e.g., the approach, explain plans (including dynamic explain), 

Version Vantage 2.2

and 17.05

July 2020

Data Engineering Exam Objectives

TERADATA.COM

additional indexes, data distribution, predicates, statistics, etc.) Additional training here

and here.

• Identify characteristics and implications of advanced tuning options that improve

performance (e.g., IPE, PRPD, and Query Rewrite, etc.)

• Given a DBQL implementation, identify how to make use of query performance data in

designing and tuning an application (e.g., using production data in development, etc.)

• Identify the design options and characteristics of an optimized application (e.g., elements

of master data, flexibility, extensibility, resiliency, portability, simplicity, etc.) Additional

training here.

• Given a scenario with a data quality issue, identify the function(s) that resolves the issue.

Additional training here.

• Identify use cases for the output from data profiling.

Vantage Integration and Interoperability – 11%

• Identify the steps and considerations to move data from object storage.

• Identify the steps and considerations to move data to object storage.

• Identify the steps and considerations to leverage data in third-party data storage.

• Identify the steps when building and operarationalizing a predictive model pipeline.

• Identify the steps when building and operarationalizing a data pipeline.

• Identify how Vantage supports event-driven architecture. Additional training support here.

• Identify the tools and features Vantage uses to integrate with native cloud services.

• Identify the features of Vantage that enables OpenSource integration. Additional training

here and here.