What is the duration set for the exam?

The exam duration is set to 90 minutes.

What concepts are expected to be covered in the second major question of the exam?

The second question will involve applied theory and practical problem-solving related to ETL processes, such as fixed-range extraction, rather than purely definitional questions.

What are the three types of data dimensions (measures) discussed in relation to summation capabilities?

The three types are Additive (SDT), Semi-Additive (Semi-SDT), and Non-Additive (Non-SDT).

If a measure is Non-Additive (Non-SDT), what is the consequence when attempting to sum it across all dimensions?

If it is Non-Additive, summation across all dimensions cannot be performed as it leads to incorrect data due to structural limitations.

What is the difference between a measure stored directly in the Fact table versus a Calculated Measure?

Measures stored directly are loaded during ETL and can be summed quickly. Calculated measures are not stored but are computed dynamically upon querying (e.g., when running MDX), often used for ratios or metrics that aren't easily summable across all dimensions.

If an attribute from the source data is descriptive but not requested in any analysis requirement, should it be included in the DDS dimension?

No, the DDS should only include dimensions necessary to serve the specified analytical purposes; unnecessary dimensions should be excluded.

[BI] - Final Exam Review - Youtube AI Summary

Exam Structure and Content Focus
📌 The exam duration is 90 minutes, structured with two large questions.
❓ One question focuses on Modeling (Modelling), and the other involves theoretical questions with applied theory to solve problems.
❌ Pure definition questions (e.g., "What is Data Mart?") will not be asked; application of theory is key.

Data Extraction and Loading (ETL) Concepts
➡️ Fixed Range ETL: Requires extracting data within a defined range, necessitating a monotonically increasing attribute (like an auto-incrementing ID) for tracking the maximum extracted ID in metadata.
➡️ Incremental Extract: Depends on having a timestamp attribute in the source data to define the extraction window based on time.
➡️ Dimension and Fact Design: Dimensions must only contain descriptive information. Measures in the Fact table must be numerical values (e.g., Sales Amount, Quantity); textual data should not be placed in measures.

Data Warehouse Modeling (Dimensional Modeling)
📊 Fact Table Measures Classification: Measures are categorized as Additive (SDT), Semi-Additive (Semi-SDT), or Non-Additive (Non-SDT) based on their aggregation capability across all dimensions.
💡 Non-Additive Measures: These (like ratios/percentages) should generally be calculated at query time using Calculated Measures rather than being stored directly in the Fact table.
❄️ Snowflake Schema: Requires normalizing dimensions into separate sub-dimension tables (e.g., separating Product Type into its own table).

Key Points & Insights
➡️ Application Focus: Prepare by practicing applying ETL methods (Fixed Range, Incremental, By Table) to provided case studies, understanding the minimum conditions required for each method.
➡️ Mandatory Passing Score: Students must achieve the minimum required score (e.g., 3 or 3.5 points) on the final exam to pass overall, regardless of high process scores.
➡️ DDS Design for Analysis: The Dimensional Data Store (DDS) must be designed specifically to serve the stated analytical requirements; not all source attributes need to be included.

📸 Video summarized with SummaryTube.com on Jan 18, 2026, 09:45 UTC

[BI] - Final Exam Review

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