CompTIA Data+ Practice Test (DA0-002)

The requirement is to keep every historical version of a dimension member, tie each fact row to the correct version, and identify the current record with a simple flag or NULL end date. SCD Type 2 meets all of these needs by inserting a new row (with a new surrogate key) whenever an attribute changes while retaining the prior rows. Type 1 overwrites values and loses history. Type 3 stores only a limited number of previous values in extra columns. Type 4 moves history to a separate table, none of which guarantee full, row-level history in the base dimension tied to facts via distinct surrogate keys.

A monitor that compares the current statistical distribution of diagnosis-code values with a historical baseline is designed to surface gradual drift. By tracking metrics such as frequencies, mean, variance, or standard deviation over time, the monitor can issue alerts when the distribution shifts beyond an acceptable confidence band. Static checks like NOT-NULL rules, regex format validation, or referential-integrity tests each protect other quality dimensions (completeness, syntactic validity, relational consistency) but will not detect a legitimate yet drifting pattern of values. Therefore, the distribution-based anomaly-detection monitor best satisfies the requirement.

A REST API is specifically designed for system-to-system data exchange. Because the carrier's endpoint returns machine-readable JSON, the analytics team can ingest the data with little transformation and schedule calls as frequently as needed, achieving near-real-time updates. The SFTP CSV and weekly XLSX options introduce batch latency that violates the five-minute requirement, while the HTML option relies on web scraping, adding fragility and extra parsing work. Therefore, the authenticated REST API is the best fit.

Scala is the language in which Apache Spark is primarily written, so developing Spark jobs in Scala avoids the Python-to-JVM bridge that Py4J introduces, removes an entire layer of serialization overhead, and lets developers use the DataFrame and strongly typed Dataset APIs as soon as they are available. Python, R, and Java can all be used with Spark, but they either rely on a bridging layer (Py4J for Python, rJava for R) or lag slightly behind Scala in API updates. Java is close, yet Spark's higher-level APIs and examples are maintained first in Scala, making Scala the best fit for the stated requirements.

Exporting the visuals to a properly tagged PDF that uses high-contrast colors and includes concise alt text meets all stated constraints. A tagged PDF carries a logical reading order and metadata that screen readers can parse, while alt text conveys the meaning of charts for users who cannot see them. High-contrast colors improve legibility for low-vision readers and satisfy WCAG contrast thresholds.

The interactive HTML5 dashboard is disallowed because the site only accepts static files, and hover-only interactions are not keyboard or screen-reader friendly. The color-coded Excel workbook fails WCAG 1.4.1 because color is the sole cue and offers no alt text or meaningful structure. The narrated screencast, lacking captions or a text alternative, excludes deaf or hard-of-hearing users and cannot be parsed by screen readers, so it does not satisfy the accessibility requirement.

The correct answer is a graph database. Graph databases are purpose-built to store and navigate relationships. Data is modeled as nodes (e.g., users, posts) and edges (e.g., 'friend', 'liked'), making them extremely efficient for traversing complex and deep relationships, which is a core requirement for identifying influencers in a social network.

A document database is incorrect because while it handles flexible schemas and user profiles well, it is less efficient than a graph database for analyzing complex, interconnected relationships between different data entities. A key-value store is also incorrect. It is designed for simple, fast lookups based on a unique key but lacks the capability to perform the complex relationship queries needed for this scenario. A column-family database is incorrect as it is optimized for analytical queries on large datasets and high-speed writes, but not specifically for traversing the types of relationships found in a social network.

The correct answer is prescriptive analytics. This method is used to determine the best course of action to achieve a specific goal. In this scenario, the model is not just describing past results or predicting future outcomes; it is actively recommending an optimal budget allocation to maximize acquisitions.

• Predictive analytics is incorrect because it focuses on forecasting what is likely to happen in the future but does not recommend a specific action. For example, a predictive model could forecast the number of acquisitions for a given budget but would not determine the optimal budget distribution itself.
• Descriptive analytics is incorrect because it focuses on summarizing past data to understand what has happened. For instance, it would be used to report on the return on investment of past campaigns but not to recommend future strategies.
• Inferential analytics is incorrect because it involves using a data sample to make generalizations about a larger population. While it might be a component of the overall analysis, the primary goal here is to optimize and recommend a specific set of actions, which is the core function of prescriptive analytics.

The correct answer is .jpg. JPEG (Joint Photographic Experts Group) files use a lossy compression method, which significantly reduces file size by permanently discarding some image data. This makes the .jpg format ideal for situations where minimizing storage and optimizing load times is critical, and a minor reduction in quality is an acceptable trade-off, especially for photographic images.

• .png: This format uses lossless compression, which preserves all original image data, resulting in higher quality but also larger file sizes compared to .jpg. It is not the best choice when the primary goal is file size reduction.
• .tiff: This format uses lossless compression or no compression at all, which preserves the highest image quality but results in very large file sizes. It is often used for professional printing and archiving, not for optimizing storage space.
• .bmp: This format is typically uncompressed, leading to extremely large file sizes. It is unsuitable for this scenario where file size reduction is a key requirement.

The correct answer is the database schema. The schema serves as the blueprint for a relational database, formally defining its structure, including tables, the columns and data types within those tables, and the relationships between them (such as primary and foreign keys). A data dictionary provides descriptive information and business context about the data elements but is not the formal structural blueprint itself. A fact table contains quantitative business metrics and is a component within a schema, not the overall blueprint. A bridge table is a specific type of table used to resolve many-to-many relationships and is also a component within a schema.

The column must store multilingual text, so a Unicode-capable data type is mandatory. In SQL Server, the Unicode options are nchar and nvarchar.

• nchar(200) allocates a fixed 200 characters (400 bytes) for every row, wasting space on shorter names.
• nvarchar(200) is Unicode and variable-length; it stores only the bytes required for each value plus a small length indicator, so short names consume far less space.
• char(200) and varchar(200) are non-Unicode in most collations (unless UTF-8 is explicitly enabled), so they cannot reliably hold Chinese or Arabic characters.

Therefore, nvarchar(200) is the best fit because it supports full Unicode and minimizes storage for values that are typically shorter than the declared maximum.

The most likely cause of an implausible 215 % margin is an error in the KPI's formula-such as dividing by the wrong column or applying an incorrect aggregate. A calculation review focuses on auditing the measures, expressions or DAX used in the report, enabling the analyst to confirm or correct the logic quickly. A peer review that concentrates on layout would not inspect the math, a code review of the entire ETL pipeline is time-consuming and unnecessary when other metrics look fine, and an alert only signals the symptom without revealing the root cause.

The predicate IS DISTINCT FROM returns TRUE only when the two compared expressions differ, treating NULL as a normal value. Because the query returned no rows, it means there is no record in which Customer_ID and CRM_Customer_GUID differ (including no mismatched NULLs). Therefore the two columns carry identical information for every row, so retaining both adds unnecessary storage and maintenance overhead-one of them can be removed. The other options misinterpret the result: a lack of mismatches does not imply duplicate rows, identify different business keys, or reveal a foreign-key relationship. It simply shows the columns are equivalent and hence redundant.

The correct answer is a dynamic dashboard. The scenario's key requirement is for a deliverable that allows the leadership team to interactively filter and drill down into the data during their meetings. A dynamic dashboard is specifically designed for this purpose, offering interactive elements like slicers, filters, and drill-down capabilities that allow users to explore the data in real time.

A static dashboard is incorrect because, while it can display the high-level KPIs, it presents a fixed, non-interactive snapshot of the data and would not allow for real-time exploration during the meeting. A recurring static report would be delivered on a schedule, such as quarterly, but it would still be a fixed report without the interactive features needed for live analysis. An executive summary is a narrative document that provides a high-level overview and key findings; it is not an interactive data exploration tool.

pandas.json_normalize is purpose-built to transform semi-structured or deeply nested JSON data into a flat, two-dimensional DataFrame. It automatically traverses dictionaries and lists, creating column headers that reflect the nested path. melt reshapes existing DataFrames from wide to long format, pivot_table summarizes data through aggregation, and read_html imports HTML tables-none of these functions flatten nested JSON. Therefore, json_normalize is the correct choice.

The correct answer is the median. In a dataset with a skewed distribution, such as salary data with a few very high earners, the median is the most appropriate measure of central tendency. The median represents the middle value and is not significantly affected by extreme outliers. The mean would be pulled upwards by the high executive salaries, giving a misleadingly high representation of a 'typical' salary. The standard deviation is a measure of data dispersion or spread around the mean, not a measure of central tendency. The mode, which is the most frequent value, is often not suitable for continuous data like salaries and may not provide a meaningful 'typical' value.

The correct regular expression is SKU:([A-Z]{3}-\d{5}). The parentheses () create a 'capturing group.' This tells the regex engine to match the entire pattern but to specifically capture and return only the portion of the string that matches the pattern inside the parentheses. In this case, it matches 'SKU:' but only captures the [A-Z]{3}-\d{5} part.

• (SKU:[A-Z]{3}-\d{5}) is incorrect because the capturing group includes the 'SKU:' prefix, which the requirement states should be excluded from the final extracted value.
• SKU:[A-Z]{3}-\d{5} is incorrect because it will find and match the pattern, but it lacks a capturing group (), so it will not isolate and extract only the identifier part.
• SKU:([A-z]{3}-\d{5}) is incorrect because the character range [A-z] is a common mistake. In ASCII, this range includes non-alphabetic characters that fall between 'Z' and 'a' (such as '[', '', ']', '^', '_', ''). The requirement specifies only uppercase letters, for which [A-Z]` is the correct and precise range.

The correct answer is to incorporate the company's logo and official color palette as defined in the style guide. Adhering to a corporate style guide, especially for materials shared with external partners, is crucial for maintaining brand identity, consistency, and professionalism. The style guide provides the specific elements, such as logos and color palettes, that create a recognizable and trusted look and feel.

• Optimizing data queries is a technical task related to performance, not a visual design element for branding.
• Using a color-blind-friendly palette is a best practice for accessibility, but when a strict corporate style guide is provided, the primary requirement is to follow those branding rules. Ideally, the corporate palette would already be accessible, but the analyst's first duty is brand alignment.
• Selecting complex charts does not address branding; the goal of chart selection should be clarity and accurate data representation, not visual complexity for its own sake.

The report is measuring consistency. A key aspect of consistency is referential integrity, which ensures that values meant to link records across different tables are valid. In this case, the profiler is calculating what percentage of foreign-key values (SalesFacts.ProductID) have a matching primary-key value in the related dimension table (ProductDim.ProductID). Completeness would focus on whether mandatory fields are populated, uniqueness would evaluate duplicate records, and timeliness would consider how current the data is. None of those other dimensions rely on cross-table validation.

The error indicates that at least one row in quantity_sold contains a non-numeric string ("N/A"), so SQL Server cannot implicitly convert the value to an integer. According to data-validation best practices, the analyst should verify and cleanse the source data before it is loaded or cast. By validating the incoming extract and filtering or correcting non-numeric values, the analyst removes the root cause of the conversion failure and prevents the error from recurring.

• Enabling detailed query logging would show the failing statement but would not fix the data quality problem.
• Increasing server memory does not address the data-type mismatch.
• Rewriting the view with a different join type does not change the fact that quantity_sold contains invalid characters.

Therefore, validating and cleansing the source data is the most appropriate first step.

Regulators typically need to know the nature and scope of the breach before anything else. Article 33(3)(a) of the GDPR, as well as comparable U.S. state breach-notification statutes and FTC guidance, explicitly require that a breach report describe the categories of personal data involved and the approximate number of records or individuals affected. Without this information, the DPO cannot assess the level of risk or determine whether external notification is legally required.

• The correct option provides the mandated information (type of data and record count).
• The other options may be useful later in an investigation (root-cause analysis, cost estimates, marketing details) but are not prerequisites for determining regulatory reporting obligations at the initial stage.