CompTIA Data+ Practice Test (DA0-001)

Foreign keys referencing records in other tables enforce consistent relationships. Primary indexing organizes records in a single table, but does not confirm valid cross-table connections. Column partitioning focuses on dividing data by columns, not on verifying data consistency among tables. Denormalization combines redundant data fields, which may cause contradictions unless designed very carefully.

When performing a complete reload cycle, a full dataset is ingested from scratch, ignoring previous data. A smaller incremental approach, often referred to as a delta load, fetches changes. Understanding this difference is crucial when defining data ingestion pipelines, such as those using an Application Programming Interface (API).

A script-driven environment or headless browser can execute the code that modifies the page after the main load, allowing the tool to see the resulting, updated content. Simply pulling the HTML page data does not capture changes introduced by those scripts. Direct access to the database is rarely offered, and server logs do not capture how the final content looks.

A design centered on individual transactions is suited for frequent data changes. It updates records for each purchase, preserving data accuracy. Star schemas handle analytics with fewer updates, while streaming engines with end-of-day metrics and data lakes for unstructured data are not as effective for continuous row-level operations.

The approach that groups dimension information into multiple smaller tables is referred to as a snowflake schema. It structures related attributes in sub-tables, which reduces the chance of duplicated fields and preserves data integrity. The alternative choices combine dimension fields into larger tables or remove them, which conflicts with the snowflake design.

A relational database uses structured tables with defined columns and rows to store data. It enforces data integrity with constraints such as primary keys and foreign keys, creating clear links between multiple tables. Non-relational databases and NoSQL databases rely on flexible document or key-value formats, which do not use strict constraints. Data lakes store diverse raw data in various formats, and data marts are specialized subsets designed for focused analytics.

A system that consolidates historical records in one place supports advanced queries on aggregated information. This is a common characteristic of OLAP (Online Analytical Processing). Other choices focus on immediate updates, real-time ingestion, or direct file scanning, which are less effective for multi-dimensional analysis of summarized data.

Retrieving the data using an authentication token through the service’s interface ensures a stable and secure exchange supported by the data provider. Other techniques rely on structure that can change or require additional manual effort, which can disrupt or delay the process and do not apply to using an API as the question states. Copying entire repositories often leads to excessive overhead and may include unrelated information, while script-based HTML parsing can break if the page layout changes.

The practice of using specialized scripts to gather details from websites is called web scraping. It fetches content automatically for further study. Survey involves questioning participants, sampling focuses on selecting subsets of people or records, and observation requires direct monitoring of actions or events.

A data lake is well-suited for storing unprocessed inputs from various systems and permits broad analytics.

A data mart focuses on a specific department, often storing structured summaries.

A transactional system handles day-to-day activities, not extensive analytics.

A data warehouse generally stores refined records for systematic reporting and analysis. The best choice is the one that holds raw information for wide-ranging analysis.

When a dimension creates additional records for attributes that change, it retains both older and newer versions. This design supports tracking previous states over time. Some approaches replace data outright, which discards past details, so they do not keep historical information.

ETL stands for Extract, Transform, Load (ETL). This method involves retrieving data, making necessary changes, and loading it into a final destination. Delta load gathers updated records instead of the initial dataset. API data retrieval refers to fetching details through an interface and does not typically include transformations or loading. Data profiling inspects data conditions without shifting it to a different location.

Delta load updates the existing dataset with records that have changed since the previous load. This helps reduce processing by avoiding reloading records that have not changed. Gamma Sync is not a real term, data reset removes the prior records entirely, and aggregation collapses records into summarized data.

Non-relational systems rely on flexible methods, such as document and key-value stores, where data structures are not tightly bound to predefined columns. Relational databases, on the other hand, typically require structured tables with well-defined schemas. Because System X is described as document-oriented, it matches non-relational principles.

The star design (often called a star schema) uses a central fact table connected to dimension tables that are not heavily normalized. This creates a clear structure for analytics, while similar approaches like snowflake arrangement feature more normalization in dimension tables and require additional relationships. Other options listed do not accurately describe this simplified structure.