CompTIA Data+ Practice Test (DA0-001)

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 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 delta load (also called an incremental load) captures just the changes-new, updated, or deleted records-since the last extraction and merges them into the target. A full or complete reload replaces the entire dataset, while schema migration and data archiving serve different purposes altogether.

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.

A delta load, also known as an incremental load, is the process of loading only the new or changed data since the last time the process was run. This is much more efficient than a full load, which reloads the entire dataset. A snapshot load captures the state of the data at a single point in time, and data blending involves combining data from different sources.

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.

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.

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.

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.

The correct answer describes a Slowly Changing Dimension (SCD) Type 2. This approach preserves history by creating a new record for each change to a specific attribute, which allows for historical tracking. SCD Type 1 would overwrite the existing record, losing the historical data. Online Transactional Processing (OLTP) systems are typically sources of data for a data warehouse but do not describe this method of managing historical data. A star schema is a database organizational model and not a method for handling attribute changes within a dimension.

Non-relational databases, which include document-oriented databases, are designed for flexibility. They store data in formats like documents or key-value pairs, which do not require a predefined schema with fixed columns and rows. This contrasts with relational databases, which enforce a strict, table-based structure. Since the system uses flexible, document-like structures, it is a non-relational database.

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.

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 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.

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.