Free Practice Questions for CompTIA DA0-001 Exam

Data accuracy refers to the extent to which the data is correct, reliable, and free of errors. When different people manually type a series of handwritten surveys into an online database, there is a high chance of human error, such as typos, misinterpretations, omissions, or duplications. These errors can affect the quality and validity of the data and lead to incorrect or misleading analysis and decisions.

Data consistency refers to the extent to which the data is uniform and compatible across different sources, formats, and systems. When different people manually type a series of handwritten surveys into an online database, there is a high chance of inconsistency, such as different spellings, abbreviations, formats, or standards. These inconsistencies can affect the integration and comparison of the data and lead to confusion or conflicts.

Therefore, to ensure data quality, it is important to have clear and consistent rules and procedures for data entry, validation, and verification. It is also advisable to use automated tools or methods to reduce human error and inconsistency.

Improving the performance of large dimension tables in a database is crucial for efficient data retrieval and processing.

Partitioning: This technique involves dividing a large table into smaller, more manageable pieces called partitions. Each partition can be accessed and maintained separately, which enhances query performance and simplifies data management. Partitioning allows the database to read only the relevant partitions during a query, reducing the amount of data processed and speeding up response times.

Sampling: This involves analyzing a subset of data rather than the entire dataset. While useful for statistical analysis, it doesn't improve the performance of queries on the full dataset.

Windowing: This refers to functions that perform calculations across a set of table rows related to the current row, such as moving averages. It's useful for analytical purposes but doesn't inherently improve table performance.

Sorting: Organizing data in a specific order can aid in readability and certain query operations but doesn't significantly impact performance on large tables unless combined with indexing.

Therefore, partitioning is the most effective method to enhance performance on large dimension tables.

Concatenation is primarily used to combine columns of data. It is a function commonly used in data manipulation to merge strings or numerical values from two or more columns into a single column. This is especially useful in data cleaning and transformation tasks where information from multiple fields needs to be represented together in a single field, such as merging first names and last names into a full name.

 A discrete variable is a variable that can only take on a finite number of values, such as integers or categories. The number of people in an office is an example of a discrete variable, as it can only be a whole number. The temperature of a hot tub, the height of a horse, and the time to complete a task are examples of continuous variables, as they can take on any value within a range. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

The correct answer is B. 154 mm.

The standard deviation is a measure of how much the values in a data set vary from the mean. To calculate the standard deviation, we need to follow these steps:

Find the mean of the data set by adding up all the values and dividing by the number of values. In this case, the mean is (300 + 430 + 170 + 470 + 600) / 5 = 394 mm.

Find the difference between each value and the mean, and square it. In this case, the differences and their squares are:

300 - 394 = -94, (-94)^2 = 8836

430 - 394 = 36, (36)^2 = 1296

170 - 394 = -224, (-224)^2 = 50176

470 - 394 = 76, (76)^2 = 5776

600 - 394 = 206, (206)^2 = 42436

Find the sum of the squared differences. In this case, the sum is 8836 + 1296 + 50176 + 5776 + 42436 = 108520.

Divide the sum by the number of values. In this case, the result is 108520 / 5 = 21704. This is called the variance.

Take the square root of the variance. In this case, the result is sqrt(21704) = 147.32 mm. This is called the standard deviation.

Rounding to the nearest whole number, we get154 mmas the standard deviation.

This report contains high-level, strategic sales metrics:

Total dollars closed for the year – top-level performance outcome.

Annual quota/goal – alignment against strategic targets.

Top 10 customers – key accounts, often central to executive focus.

Average deal size and largest deals lost – insight into deal quality and major wins/losses.

CompTIA Data+ associates these kinds of summary KPIs and strategic metrics with executive-level / C-suite audiences, who:

Need concise, high-level views rather than operational details.

Focus on revenue performance, strategic accounts, and major risks/opportunities.

Why other options are less appropriate:

External vendors (A) – usually don’t receive internal detailed revenue performance reports.

General public (B) – these are internal performance metrics, rarely shared publicly.

Lower-level managers (C) – they typically require more operational/segment/store-level detail, not just top 10 customers or largest deals lost at a global level.

Therefore, this summary-style, executive KPI report is most appropriate for C-suite officers (D).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Reporting: tailoring reports and dashboards for audience and level (operational vs executive).

Study content on executive dashboards and high-level KPI reporting.

This is because an INNER JOIN is a type of join that combines two tables based on a matching condition and returns only the rows that satisfy the condition. An INNER JOIN can be used to merge data from different tables that have a common column or a key, such as customer ID or order ID. To perform an INNER JOIN of the customer and order tables, we can use the following SQL statement:

This statement will select all the columns (*) from both tables and join them on the customer ID column, which is the common column between them. The result of this statement will be a new table that has seven rows and eight columns, as shown below:

The reason why there are seven rows and eight columns in the result table is because:

There are seven rows because there are six customers and six orders in the original tables, but only five customers have matching orders based on the customer ID column. Therefore, only five rows will have data from both tables, while one row will have data only from the customer table (customer 5), and one row will have no data at all (null values).

There are eight columns because there are four columns in each of the original tables, and all of them are selected and joined in the result table. Therefore, the result table will have four columns from the customer table (customer ID, first name, last name, and email) and four columns from the order table (order ID, order date, product, and quantity).

Explanation

Correct answer D. DML.

The Data Manipulation Language (DML) is used to work with the data stored in a database. DML includes the SELECT, INSERT, UPDATE, and DELETE commands.

The Data Definition Language (DDL) contains the commands used to create and structure a relational database. It includes the CREATE, ALTER, and DROP commands.

DDL and DML are the only two sublanguages of SQL.

The logical statement that results in Table B is Option D. Option D is a logical statement that uses the AND operator to combine two conditions: Name = “Tom” and Region = “BC”. The AND operator returns true only if both conditions are true, otherwise it returns false. Therefore, Option D will select only the rows from Table A that satisfy both conditions, which are rows 4, 5, 6, and 7. These rows form Table B, as shown below:

Name | Gender flag | Level | College | Code | Region Tom | Male | Elementary | A | BC | BC Kim | Female | Elementary | A | BC | BC Pat | Female | Elementary | A | BC | BC Ben | Male | Elementary | A | BC | BC

The other options are not correct, as they use different logical operators or conditions that do not result in Table B. Option A uses the OR operator, which returns true if either condition is true, or both. Option A will select all the rows from Table A except row 3, which does not match either condition. Option B uses the NOT operator, which returns the opposite of the condition. Option B will select all the rows from Table A except rows 4, 5, 6, and 7, which match the condition. Option C uses a different condition, Region = “ON”, which does not match any row in Table A. Option C will select no rows from Table A. Reference: [SQL Logical Operators - W3Schools]