Database Management Systems

ECM means managing unstructured content (B) and enables knowledge management (C) within the organization. Option A describes CRM, and D is incorrect because ECM specifically handles UNSTRUCTURED content, not structured data.

A Document Management System (DMS) focuses on the lifecycle of electronic documents. Key features include Version Control (tracking changes), Access Control (security), and Search/Retrieval. While OCR/ICR is often used to ingest paper documents into a DMS, it is considered a capture technology rather than a core management feature of the DMS itself.

When a cluster member crashes, locks held by its transactions must be released by another member with access to the transaction log for recovery. The transaction log contains the state of in-flight transactions. Options A, B, and D don't address transaction log access which is required for lock recovery in clustered environments.

List data containers are specifically designed to display repeating, columnar data in tabular format. They show data rows with multiple columns, making them ideal for detailed listings. Summary data involves aggregations and would use summary containers, while graphical data requires chart or graphic containers.

Building a data warehouse involves Requirement gathering, Data modeling, and Data profiling as core steps. Data mining is an analytical technique performed on data, not a step in building the warehouse infrastructure itself.

Data Integration is the process of extracting data from multiple sources, cleansing it, applying business rules, and loading it into a unified target like a data warehouse. Data Profiling is analysis, Data Modelling is design, and Data Staging is temporary storage.

Data modeling is a broad concept that includes conceptual, logical, and physical modeling of data. Dimensional modeling is a specific data modeling technique optimized for data warehousing and business intelligence (using facts and dimensions). They are not the same.

All three listed techniques - Conceptual modelling, Logical modelling, and Physical modelling - are valid ER modelling techniques. Therefore 'None of the above' is the correct answer since all options ARE techniques.

In Teradata, a Global Temporary Table (GTT) stores its definition (DDL) in the data dictionary, which is shared by all users. However, the data inserted into the table is materialized in temp space and is private to the session, meaning users do not share data.

DBCS uses two bytes to represent each character, enabling support for large character sets like Japanese, Chinese, or Korean. This is necessary because single-byte encoding can only represent 256 characters, which is insufficient for languages with thousands of characters.

Fact table granularity is determined by business requirements and the level of detail needed for analysis. It should align with what the business needs from the data warehouse, not necessarily match the raw business process granularity (too detailed) or be an aggregate (too summarized).

To preserve historical changes in a dimension (Slowly Changing Dimension Type 2), you add surrogate keys to the dimension and fact tables, and insert a new row in the dimension table whenever an update occurs.

Two facts based on the same dimensions can be stored in the same fact table as long as they share the exact same level of detail (granularity).

Aggregate facts at different levels can share the same physical table as long as each fact references dimension keys at its appropriate grain. Alternatively, different aggregate levels can be stored in separate tables for performance or clarity. Option C is incorrect because pre-aggregated facts are commonly stored in physical tables rather than calculated on the fly.

Since the table was dropped with the PURGE option, it bypasses the Recycle Bin, meaning flashback drop cannot be used. You must restore all data files from the last backup and perform a time-based incomplete recovery to a point just before 8:00 a.m.