CompTIA Data+ Practice Test (DA0-001)

Data append adds additional rows to an existing structure when columns are the same in each data set. Data merge pairs records based on a matching key, data blending combines columns from multiple data sources for combined analysis, and normalization modifies the structure of data rather than focusing on adding additional rows.

Replacing the word "EMPTY" with the numeric value 0 converts the column to a consistent numeric type and keeps every record available for analysis. In this context, "EMPTY" truly indicates an out-of-stock item, so 0 is the correct business value. Deleting the rows would discard other useful attributes, converting the column to text would still block numeric summaries, and moving the rows elsewhere only postpones the problem.

Adding the revenue amounts for each product line is done with a function that sums numeric values. SUM() adds the daily amounts into a single value. AGGREGATE() is not a function in SQL. COUNT() returns how many rows are in the result set. AVG() computes an average of the daily values. To get a combined number that includes revenue amounts, SUM() is best.

Including a concise guide for reading the report and the company’s branding elements on the first page meets both the manager’s request for usage instructions and the style requirement for brand identity. A color-coded legend alone would not provide usage guidance, placing the distribution list on the final page does not address brand identity, and adding disclaimers as a footnote does not include instructions for interpreting the report.

A data use agreement is a contractual document that outlines how information can be accessed, handled, and shared between entities under specific guidelines. In this scenario, it would legally bind the research firm to the terms of data use. Data replication is a method for ensuring data availability, not for governing its use. Data encryption is a security technique to protect data from unauthorized access, while de-identification is the process of removing personal details; both are processes that would be applied to the data itself, not the agreement governing its use.

A role-based authentication gateway combined with selective data filtering protects sensitive information. Role-based methods ensure that certain user groups have privileges to access specific data fields. The other options do not provide the same targeted control, either exposing sensitive content or not addressing security requirements.

The median is the most appropriate choice because it is robust to outliers. The mean would be skewed by the extreme values, leading to inaccurate imputations. The mode is typically used for categorical data, not continuous numeric data like age. Imputing with a constant value like zero would distort the statistical properties of the age distribution and is generally not a good practice unless zero has a specific meaning in the context of the data.

Creating a saved search that includes the specified filter conditions preserves the parameters for repeated usage. Scheduling results does not store the unique filters for quick retrieval. Restricting to a particular date range does not match the need for dynamic priority or department filtering. Granting view access deals with permissions, not saving the search criteria.

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.

Reasonable expectations validation establishes what a "normal" value should look like based on past data or business rules. Incoming records are checked against those expectations, and any value falling outside the expected range is flagged for review. Cross-validation compares two different models or datasets, data profiling explores structure and statistics without necessarily setting thresholds, and data audits are broader periodic reviews rather than record-level checks of plausibility.

An external definition file (XSD) verifies tags, element types, and ordering within XML-based data. Placeholders do not require valid element behavior. Loading items as plain text on separate lines does not confirm markup consistency. Adding a standard web page has no mechanism to enforce structural constraints. With a schema, each element is checked for correct naming and content, reducing invalid entries.

A t-test is appropriate as it is designed to compare two sets of numeric results and determine if their means differ significantly. Correlation testing assesses the relationship between two variables, not differences between groups. A chi-squared test is for categorical data comparisons and is not suitable for numeric datasets. A Z-score measures how one value deviates from a mean, which is unrelated to comparing two sample groups.

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.

SAS (Statistical Analysis System) is an enterprise-class solution for advanced analytics, supporting comprehensive data manipulation, statistical procedures, and reporting in a unified environment.

SQL (Structured Query Language) is incorrect as it primarily focuses on querying and managing relational databases, not offering advanced analytics features.

Apache Spark is incorrect since it specializes in distributed computing and large-scale data processing rather than business intelligence or statistical modeling.

Hadoop MapReduce is incorrect because it is designed for distributed computing frameworks aimed at processing big data, lacking integrated functionalities for predictive analysis or business intelligence.

IBM SPSS Modeler is the correct choice as it includes powerful, integrated text analytics features that use Natural Language Processing (NLP) to extract concepts and sentiments from unstructured text and convert them into structured variables for use in predictive modeling. While tools like SQL are used for querying structured data, and Tableau is primarily for data visualization, SPSS Modeler is specifically built for advanced data mining tasks like this.

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 (evaluating text as 0), which can alter the result from a pure arithmetic mean of numbers.

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.

Link analysis identifies relationships among items and how they connect, potentially uncovering pathways that are not immediately visible. Trend analysis focuses on how values change across intervals, performance analysis measures outcomes against goals, and exploratory data analysis uses descriptive statistics to uncover overall patterns without mapping interconnections among data points.

This type is useful for storing names or phrases that do not require mathematical operations. Numeric handles values for arithmetic. Currency handles monetary amounts. Date manages calendar-specific information.

Jurisdiction requirements refer to the need for an organization to comply with the industry and governmental regulations that apply in every location where data is collected, stored, or processed. Meeting these requirements may involve localizing data storage, adjusting consent forms, or honoring regional breach-notification rules. Entity relationship constraints, data quality metric audits, and role assignment policies address other governance concerns but do not deal specifically with location-based legal compliance.