CompTIA Data+ Practice Test (DA0-001)

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.

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.

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 queue-based method regulates how frequently calls are sent, which prevents exceeding service-request thresholds. Increasing parallel threads raises the risk of additional rejections. Changing the data format does not address the issue of exceeding limits. Caching existing data can help with repeated queries against local content, but it does not fetch fresh data needed for daily updates on its own.

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.

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

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.

Data profiling detects unusual patterns, missing fields, and inconsistencies in publicly sourced information, which helps produce a unified and robust dataset when merging with existing tables. Overlooking structure, neglecting validation, or removing incomplete entries may lead to overlooked anomalies or data loss.

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 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.

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.

The described model is a star schema. Its main advantage is improved query performance for analytical workloads. This is because its denormalized structure requires fewer complex joins to retrieve data compared to a highly normalized (3NF) OLTP schema. While a snowflake schema, which normalizes the dimension tables, can reduce storage space, it introduces more joins and complexity, potentially slowing down queries. A fully normalized schema is optimized for transactional speed and data integrity, not large-scale analytics. An unstructured model would not be suitable for this type of relational, transactional data.

Replacing older data with updated details ensures the table reflects current roles. Creating new rows, keeping a separate table for historical entries, or adding multiple columns preserves past data, which goes against the requirement to discard older records.

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 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.

Retaining older dimension details by generating a new record preserves historical data needed for thorough analytics. The other answers either overwrite older rows, store partial updates, or place older data in a temporary store that limits comprehensive historical analysis.

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.

ETL stands for Extract, Transform, Load. This method involves retrieving data (extract), making necessary changes (transform), and loading it into a final destination (load). 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 star design (often called a star schema) uses a single fact table surrounded by denormalized dimension tables that connect only to the fact table, not to each other. This simple layout speeds up analytical queries, while a snowflake schema normalizes dimension tables, increasing joins and complexity. The other listed options are not standard dimensional models that provide this structure.

Foreign keys referencing records in other tables enforce referential integrity, ensuring that each appointment row must correspond to existing doctor and patient rows. Primary indexing organizes data within a single table but does not check cross-table links. Column partitioning improves storage layout rather than relationship integrity. Denormalization merges redundant data and can introduce inconsistencies unless extra safeguards are used.