CompTIA DA0-001 - CompTIA Data+ Certification Exam

For efficient data manipulation, the ideal order would be to first merge related tables to create a comprehensive set of records, then deduplicate to remove any redundant information. Lastly, appending additional data, such as from another source or table, ensures that all relevant data is included without redundancy before the final analysis. This order prevents unnecessary duplication of effort, such as deduplicating both before and after appending, which would be less efficient.

In the context of the tables provided, merging would likely involve combining customer information from the online and in-store transaction tables with the customer table. Deduplication would remove any redundant customer records that may exist across these tables. Finally, appending would involve adding any additional transaction records to the master file, ensuring a complete dataset for analysis.

Although the specific table isn’t shown here, this question type in CompTIA-style practice typically presents a score table where some values:

Are outside the valid range (e.g., scores > 100 on a 0–100 test, or negative scores)

Or use non-numeric or nonsensical entries for a score field (e.g., “N/A”, “ABC” where numeric values are required)

When values violate defined business rules or constraints (e.g., “exam scores must be between 0 and 100 and numeric”), they are considered invalid data.

Why the other options are not the best reason:

Redundant data (B): Would mean repeated/duplicate copies of the same information. That doesn’t address bad or impossible values.

Data outliers (C): Outliers are extreme but possible values within the defined range. Here the driver for cleansing is not just extremity but rule violation (e.g., >100 on a test that maxes at 100).

Missing data (D): Would concern null/blank fields, which is a different quality issue.

The key phrase in this type of question is that some values do not conform to the allowed or logical rules of the field. In CompTIA Data+ terms, this is a classic case of Invalid data (A) and is a primary driver for data cleansing.

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Domain: Data Quality (identifying invalid, inconsistent, incomplete data).

CompTIA Data+ Official Study Guide – section on data quality dimensions, highlighting validity as adherence to defined business rules (e.g., allowed ranges for scores).

The answer is D. Data sources and attributes.

Short explanation: The data analyst should consider the data sources and attributes first when creating a dashboard, because they determine what kind of information can be included and how it can be displayed. The data sources and attributes define the origin, quality, format, and structure of the data that will be used for the dashboard.They also affect the data refresh rate, the consumer types, and the access permissions of the dashboard12

A. Data refresh rate is not the first thing to consider, because it depends on the data sources and attributes. The data refresh rate is how often the data in the dashboard is updated or refreshed to reflect the latest changes.The data refresh rate can vary depending on the type, frequency, and availability of the data sources1

B. Consumer types are not the first thing to consider, because they depend on the data sources and attributes. The consumer types are the intended audiences or users of the dashboard, who may have different needs, preferences, and expectations for the dashboard. The consumer types can influence the design, layout, and functionality of the dashboard.However, the consumer types cannot be determined without knowing what kind of data is available and relevant for them1

C. Access permissions are not the first thing to consider, because they depend on the data sources and attributes. The access permissions are the rules or policies that govern who can view, edit, or share the dashboard. The access permissions can protect the confidentiality, integrity, and availability of the data in the dashboard.However, the access permissions cannot be set without knowing what kind of data is involved and who needs to access it1

Personally Identifiable Information (PII) refers to any data that can be used to identify a specific individual. To comply with privacy policies, it's crucial to mask such information to protect individual privacy.

Option A:Government-issued ID

Rationale:Government-issued IDs (e.g., Social Security numbers, driver's license numbers) are direct identifiers and are considered sensitive PII. Masking this information prevents unauthorized access to critical personal data.

Option B:Address

Rationale:An individual's address is also considered PII, as it can be used to locate and identify a person. Masking addresses helps in protecting personal location information.

Option C:Order ID

Rationale:An order ID is typically a unique identifier for a transaction and does not directly identify an individual.

Option D:Order date

Rationale:The date of an order does not constitute PII, as it doesn't provide information that can identify a person.

Explanation

Transpose creates a new data file in which the rows and columns in the original data file are transposed so that cases (rows) become variables and variables (columns) become cases. Transpose automatically creates new variable names and displays a list of the new variable names.

Transposing data is useful for data analysis. At times, we have to pull data from various files with different formats for analysis and preparing reports. In such circumstances, we may have to transpose some data from one file to the other. In excel, we can transpose data in multiple ways.

Explanation

A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.

What is the mode?

The mode is the most commonly occurring value in a distribution.

The most commonly occurring value is 54, therefore the mode of this distribution is 54 years.