CompTIA Data+ Practice Test (DA0-001)

They do not follow a rigid, consistent pattern and can contain varying information, which indicates they are unstructured. Structured options rely on fixed layouts and consistent fields, so the information described does not match a format with defined rows and columns or hierarchical tags.

The specialized structure needed here is known as an index. By creating a direct reference path, queries locate matching rows faster. Splitting large tables into sections helps manage data quantity but does not build a direct lookup path for repeated column queries. Removing repeated data from the design (normalizing) prevents redundancy yet does not specifically accelerate searches. Applying hashes to columns transforms values but does not map entries to rows for quicker retrieval.

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.

They enable flexible methods of displaying the same dataset in a single interface. With this feature, one can switch to different slices of the data instead of building multiple reports. The incorrect choices involve various user permission settings, collaboration groupings, or column references, which do not provide alternate display modes for the same dataset.

AVERAGE computes the arithmetic mean by adding the values and dividing the sum by the total number of values.

SUM only adds the values without calculating a mean.

MEDIAN returns the middle value in a sorted list and does not calculate a mean.

AVERAGEA handles cells differently, including text or logical values, which can alter the result and cause unexpected outcomes.

Placing the reformatted version in a separate column helps preserve the initial data for verification or troubleshooting. Overwriting existing data removes the ability to compare older data with the new standardized formats. Delaying transformations to a later stage can cause misaligned information if earlier operations rely on consistent formats. Splitting transformations in steps may produce inconsistent data if partial changes happen multiple times.

Senior leadership typically focuses on broad organizational performance metrics and strategic objectives. Highlighting major goals and key performance measures supports decision-making at the highest level. Detailed product logs, compliance forms, or in-depth technical metrics lack the concise and high-level overview these leaders seek.

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.

Placing the data refresh date in a prominent location, such as next to the main heading, makes it immediately visible and prevents confusion over the data's timeliness. Hiding the date in a footnote or technical documentation can lead to it being overlooked. Relying on the file system's 'Date modified' timestamp is unreliable, as this metadata can change for reasons other than a data refresh, such as a simple formatting adjustment. Placing the date in a chart legend does not give it the prominence needed for quick reference, as the legend's primary purpose is to explain the data encoded in the chart.

One platform features a drag-and-drop interface that connects to various sources to quickly create dashboards. Another solution is primarily a query language with no native dashboard creation interface. Another is a programming environment used for data manipulation and analysis but requires more coding. Another is commonly used for detailed statistical modeling. The correct choice in this scenario blends accessible dashboard creation with easy data source connections, making it convenient for users who lack extensive coding expertise.

The correct answer identifies Stata as a statistical software package used for data analysis, management, and visualization. The other options describe either unrelated software or incorrect functionalities. For example, referring to Stata as exclusively a database management system or a spreadsheet software misrepresents its primary purpose.

A transformation procedure referencing standard conventions applies consistent rules for field names and date formats across all records. Manual edits can create inconsistent outcomes over time. Spreading tasks across various spreadsheets does not guarantee uniform updates. Exporting data as text files and reimporting does not systematically apply a standard naming scheme or date formatting rules.

The Z-score (also called a standard score) is found with the formula (value − mean) / standard deviation. The resulting number tells you precisely how many standard deviations the observation is above or below the mean. Variance squares the deviations and averages them, standard deviation is the square root of variance, and the interquartile range measures the spread of the middle 50 % of data-none of these convey the standardized distance of a single data point from the mean.

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.

The correct answer is HIPAA Policy. The Health Insurance Portability and Accountability Act (HIPAA) sets the nationwide standard in the U.S. for protecting sensitive patient health information (PHI) through specific privacy and security rules. Telehealth Expansion Policy focuses on regulations for remote healthcare delivery, not the comprehensive storage requirements for all patient records. PCI DSS (Payment Card Industry Data Security Standard) Policy applies to protecting credit card data, not patient health records. A Medical Data Management Policy is a generic term; while important, it is the HIPAA-specific policy that meets the legal requirements described in the scenario.