Description

Exam Name: Teradata TDBL4-16.20 Exam
Exam Code: TDBL4-16.20
Related Certification(s): Teradata 16 Certified Advanced DBA
Certification Provider: Teradata
Number of TDBL4-16.20 practice questions in our database: 91 Updated Questions

Expected TDBL4-16.20 Exam Topics, as suggested by Teradata

Topic 1: Advanced Teradata Architecture and Internals
This topic dives deep into Teradata’s advanced architecture, covering hashing algorithms, AMP operations, BYNET efficiency, and system resource utilization. It also focuses on multi-temperature data management and workload distribution.

Topic 2: Performance Optimization and Indexing Strategies
Understanding key performance tuning techniques, including the role of primary and secondary indexes, join indexes, partitioned primary indexes (PPI), and system statistics collection. It also discusses query rewrites and optimizer strategies.

Topic 3: Workload Management and System Monitoring
Exploring Teradata’s workload management features, including Teradata Active System Management (TASM), QueryGrid, and priority scheduling. It also covers the use of Viewpoint and DBQL for real-time monitoring.

Topic 4: Data Security, User Management, and Access Control
Focusing on authentication methods, role-based security, encryption, user privileges, object-level security, and best practices for securing sensitive data within a Teradata environment.

Topic 5: Advanced SQL and Query Performance Tuning
Covering complex SQL optimizations, query parallelism, join strategies, temporary tables, and macros. It also explains handling skew issues and using Explain plans for troubleshooting.

Topic 6: Backup, Restore, and High Availability Strategies
Understanding Teradata’s disaster recovery mechanisms, including Dual Active solutions, Unity Director, fallback tables, and restoring data using ARC and BAR (Backup and Restore) utilities.

Topic 7: Data Integration and ETL Optimization
Exploring Teradata utilities such as FastLoad, MultiLoad, TPump, TPT (Teradata Parallel Transporter), and their role in large-scale data ingestion. It also discusses ETL best practices for managing workloads efficiently.