CompTIA Data+ Practice Test (DA0-002)

Using a deterministic one-way hash (or similar tokenization approach) on customer identifiers converts each value into an irreversible surrogate that is identical every time the same input appears. This keeps PII out of the file while still allowing the vendor to join rows that refer to the same shopper. Encrypting the entire file and then handing over the key simply reveals the raw PII once it is decrypted. Aggregating the data removes the ability to link individual shoppers across days, preventing the required analyses. Partially masking names or card numbers leaves enough of the original values to risk re-identification and still exposes other direct identifiers such as email addresses.

Dimensional modeling guidelines recommend assigning each dimension row a meaningless, sequential surrogate key-typically a 4-byte integer-rather than relying on natural business keys or wide composite keys. A compact surrogate key keeps the fact table's foreign-key columns small (saving space and index overhead), speeds join performance, shields the warehouse from changes to source-system codes, and is mandatory when tracking slowly changing dimension history. Natural keys, GUIDs and composite keys all consume more space, may change unexpectedly, and complicate surrogate-key lookups for historical versioning, so they are not preferred choices for a dimension table in a star schema.

The correct answer is encryption in transit. This practice specifically addresses the protection of data as it moves from one location to another, such as across the internet or a private network. Technologies like TLS (Transport Layer Security) are used to create a secure, encrypted channel to prevent eavesdropping or interception during the transfer, which directly addresses the requirement in the scenario.

• Encryption at rest is incorrect because it applies to data that is stored on a physical or virtual medium, like a hard drive or in a database, not data that is actively moving across a network. While important for securing the data before and after the transfer, it does not protect the data during the transfer.
• Data masking is incorrect as it involves obscuring specific data elements by replacing them with fictitious but realistic-looking data. This is useful for creating non-production datasets for testing or development but does not encrypt the entire dataset to protect it from interception during transmission.
• Role-based access control (RBAC) is an incorrect choice because it is an authorization mechanism that defines who can access specific resources based on their job function. It controls access to the endpoints (the database and the cloud platform) but does not protect the data itself from being intercepted as it travels between those endpoints.

A derived variable (also called a calculated or derived column) is a new field produced by applying an expression to existing columns and leaving the source data intact. In SQL Server the DATEDIFF() function returns the integer difference between two dates in the specified unit, so DATEDIFF(day, order_date, delivery_date) yields the total transit time for each row. Aliasing that result as shipping_days adds the new field without altering order_date or delivery_date, satisfying the scenario.

The second choice misuses DATEADD, which adds an interval to a date instead of computing a difference. The third choice subtracts two datetimes directly; in SQL Server this produces a decimal in days only when both are FLOAT expressions, not a clean integer, and it is unreliable across platforms. The fourth choice reverses the start and end arguments in DATEDIFF, resulting in negative values for on-time shipments and thus failing the business rule.

The correct answer is that a .txt file format does not store metadata or formatting, requiring the analyst to determine the data's structure. A .txt file is a plain text file, meaning it contains only text characters without any embedded information about formatting (like bold or italics), or structure (like defined columns). When ingesting data from a .txt file, the analyst must inspect the file to understand its layout, such as whether the data is separated by commas, tabs, or another delimiter, and determine the correct character encoding (e.g., UTF-8, ANSI) to read the characters correctly.

The other options are incorrect. Describing data in a nested, hierarchical structure is characteristic of a .json or .xml file. A file that is a binary format requiring a specific driver is typical of proprietary database files, not universally readable text files. Lastly, while a .txt file might have a header row, this is simply the first line of text and is not inherent schema information enforced by the file format itself.

The correct answer is Timestamp. The TIMESTAMP data type is ideal for global applications because it stores date and time values by converting them from the client's time zone to a standardized format, typically Coordinated Universal Time (UTC). When the data is retrieved, it can be converted back to the local time zone of the session, ensuring all events are comparable on a single timeline.

• DATETIME is incorrect because it typically stores the literal date and time value ('YYYY-MM-DD HH:MM:SS') without any time zone information. This can lead to ambiguity when data is collected from multiple time zones, making accurate chronological analysis difficult.
• String (e.g., VARCHAR) is incorrect because storing dates as text is inefficient, prone to formatting errors, and makes date/time calculations and sorting computationally expensive and complex.
• Integer is incorrect because while one could store time as a Unix timestamp (an integer representing seconds since the epoch), the Timestamp data type is specifically designed for this purpose, offering better readability and more powerful built-in functions for date and time manipulation within the database.

NVARCHAR is the appropriate choice because it always stores Unicode text using UTF-16, guaranteeing support for virtually all modern languages regardless of the server's code page or collation.

• VARCHAR requires a code-page-specific or UTF-8 collation. When the server is using the default non-UTF-8 collation (as in this scenario), characters outside that code page-such as Japanese or Cyrillic-may be truncated or converted to question marks.
• CHAR is a fixed-length, non-Unicode type under the same limitations as VARCHAR and also wastes space for variable-length feedback.
• CLOB (Character Large Object) is intended for very large blocks of text and is not a standard SQL Server column type; in SQL Server the modern equivalent would be NVARCHAR(MAX). For typical feedback strings, NVARCHAR or NVARCHAR(MAX) is more flexible.

Because the missing values occur in the target variable-not in the predictor features-the rows cannot contribute to supervised learning. Imputing or transforming the missing target would inject fabricated labels and risk corrupting the model. Binning or scaling features does nothing to resolve the missing label, and the library will still fail. Given that the affected subset represents only 0.52 % of the data and its removal does not bias the class distribution, listwise deletion (dropping those rows) is the proper cleansing step. Imputing the mode would create false churn outcomes; scaling features leaves the NULLs untouched; and binning the target is impossible without a value to bin, so those choices are incorrect.

UNION ALL simply appends the rows from one query result set to another without performing the duplicate-elimination sort that a plain UNION must execute. When the analyst knows that duplicate rows cannot exist-as in separate yearly invoice tables-UNION ALL avoids the expensive sort/merge phase and therefore runs faster on large row counts. Using UNION would trigger an unnecessary DISTINCT operation; either type of JOIN would produce a wider result that repeats columns and requires join logic rather than a vertical append, adding complexity without benefit in this scenario.

The principle of least privilege states that each user should be granted only the minimum permissions necessary to perform current job functions. If membership in the Marketing_Analyst role had been removed when the intern changed jobs, she would not have retained unneeded access to marketing tables.

Separation of duties focuses on splitting sensitive tasks among different roles to avoid fraud, not on pruning excess permissions. Mandatory access control is a different, highly restrictive model enforced centrally through security labels, and attribute-based access control grants rights dynamically based on attributes rather than predefined roles. Therefore, least privilege is the only principle that directly addresses the problem of users accumulating more privileges than they require.

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.

Jurisdictional requirements focus on laws that apply because of where data is collected, stored, or accessed. Under the GDPR, Singapore is a "third country," so exporting EU personal data there is lawful only if an approved cross-border transfer mechanism-such as an adequacy decision, Standard Contractual Clauses, or Binding Corporate Rules-has been validated. Verifying and implementing that legal basis directly addresses the geographic (jurisdictional) aspect of compliance. The other options deal with technical safeguards (encryption), disaster-recovery timing, or metadata maintenance; none of those, by themselves, satisfy location-based legal obligations.

The correct answer is Scaling. Scaling, which includes techniques like normalization (Min-Max scaling) and standardization (Z-score scaling), is used to transform the values of numeric features to a similar scale. This is crucial for distance-based algorithms, such as k-means clustering, where features with larger ranges can dominate the distance calculations and skew the results. By scaling annual_income and customer_satisfaction_score to a common range (e.g., 0 to 1), the analyst ensures both features contribute more equitably to the clustering model.

• Binning is incorrect because it involves grouping a range of continuous values into a smaller number of discrete 'bins' or categories. This is used to simplify data or convert it to a categorical format, not to address the influence of different numeric scales in a distance-based algorithm.
• Imputation is the process of replacing missing values in a dataset. The scenario does not mention any missing data, making this technique irrelevant to the problem described.
• Parsing is the process of converting unstructured or semi-structured data (like text strings or log files) into a structured format. This is not applicable to transforming the scale of existing numerical features.

GDPR regards data as anonymous only when neither the controller nor any other party can single out, link to or infer the identity of an individual using means reasonably likely to be employed. The strategy that first generalizes indirect identifiers (such as converting exact dates of birth into five-year age bands) and then aggregates the records so that every published row summarises at least 50 patients-with no retained mapping key-removes both direct and indirect identifiers and eliminates the possibility of re-identification, satisfying the anonymization test in Recital 26. Replacing patient IDs with random tokens still allows re-identification through the lookup table, so it is merely pseudonymization. Encrypting the file is a security measure; because the controller holds the decryption key, the underlying personal data remain accessible and therefore subject to GDPR. Simply masking names and a portion of Social Security numbers leaves quasi-identifiers (full postcode plus exact date of birth) that can single out individuals, so the data are not anonymous.

The correct answer is to render the PAN unreadable using an approved method like truncation, tokenization, or encryption. PCI DSS Requirement 3 specifically mandates that stored Primary Account Numbers (PAN) must be protected. Requirement 3.4 states that the PAN must be rendered unreadable wherever it is stored. Acceptable methods include one-way hashing, truncation (storing only a segment of the PAN, such as the first six and last four digits), tokenization (replacing the PAN with a non-sensitive value), and strong encryption.

• Implementing role-based access control is a necessary part of PCI DSS (Requirement 7), but it is not sufficient on its own. If the data itself is stored unencrypted, a breach of an authorized user's credentials would still expose the plaintext PANs.
• Encrypting data in transit (Requirement 4) is also a key part of PCI DSS, but it does not address the risk of the data being stored insecurely at rest in the database.
• Anonymizing other personally identifiable information like the name and address is a good general data privacy practice but does not address the specific and primary PCI DSS requirement to protect the PAN itself.

VARCHAR stores variable-length, non-Unicode text and allocates only the bytes actually needed for each row (plus a small length overhead). Because most email addresses are shorter than the 320-character ceiling, a fixed-length CHAR column would waste space by padding shorter values, and NVARCHAR would double storage because it uses two bytes per character for Unicode. VARCHAR(MAX) allows strings far larger than 320 characters and has additional management overhead. Therefore, defining the column as VARCHAR(320) provides the required capacity with the smallest average storage footprint.

Best-practice frameworks such as NIST SP 800-61 and SP 800-171 state that when potential security incidents are detected, the event must be documented and reported through the organization's defined channel before containment or eradication begins. Prompt notification supplies the incident-response team with the information they need to triage, coordinate containment and forensics, and ensures that critical evidence (for example, logs, memory images, or query traces) is not destroyed. Patching a live system, powering it down, or delaying notification until after a full forensic review can compromise evidence or violate required reporting time frames.

MySQL Workbench is purpose-built for designing and administering MySQL databases. It can reverse-engineer an existing schema into an EER diagram, let you edit tables and relationships in a graphical canvas, and includes an integrated SQL editor for running queries-all within one interface. RStudio is an IDE for R/Python code, not a database design tool. Power BI Desktop focuses on building dashboards and does not create or modify database schemas. The pandas library supplies data-wrangling functions inside Python but offers no visual modeling or direct database administration features.

The Character Large Object (CLOB) data type is intended for very large blocks of text-often up to several gigabytes-while still allowing the database engine to treat the content as character data. Because the data remain in a character-encoded form, built-in SQL text operations (e.g., LIKE, SUBSTRING) can be applied. A BLOB, by contrast, stores raw binary data and does not natively support text functions. Standard VARCHAR columns are limited to a few thousand bytes in most platforms (for example, 2-4 KB) and therefore cannot hold 200 KB of text. Numeric types such as FLOAT are meant for numbers, not text. Hence, CLOB is the most appropriate choice.

The correct answer is that the file uses a proprietary or custom structure defined by the creating software. The .dat file extension is short for 'data' and is used as a generic container for information specific to an application. It has no single, standardized format. The scenario described, with a mix of readable text and unreadable binary characters, is a common indicator of a proprietary format that requires specific software or knowledge of its unique structure to be parsed correctly.

• The suggestion that the file is corrupted is a plausible but less likely primary explanation. Proprietary files are often intentionally designed with mixed text and binary content.
• While some applications might use a .dat extension for a compressed archive, it is not the default meaning. Also, a standard compressed file would typically appear as entirely unreadable binary data in a text editor, not a mix with clear headers.
• A Character Large Object (CLOB) is a data type used within a database to store large amounts of text, not a standard file format itself, making this option incorrect.