CompTIA Updated DA0-001 Exam Questions and Answers by zak

Explanation

The correct answer is B: Data blending.

Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization and can then be processed or analyzed. Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question. Data append is incorrect. Data append is a process that involves adding new data elements to an existing database. An example of a common data append would be the enhancement of a company's customer files. A data append takes the information they have, matches it against a larger database of business data, allowing the desired missing data fields to be added. Normalize data is incorrect.

Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set.

The SQL query provided is selecting all records from the Cust_table where the age column has values greater than 60 and the City column matches “New York”. The > operator selects values that are strictly greater than the comparison value, so it does not include customers aged exactly 60. The term “New York” in the context of a city database typically refers to New York City, not the state of New York. Therefore, the correct answer is that the analyst is retrieving data for all customers older than age 60 in New York City.

References:

The use of the > operator in SQL is to select values greater than the specified value1.

Understanding the WHERE clause in SQL and its use in filtering records based on specified conditions2.

Clarification on the distinction between city and state names in database records3.

Comprehensive and Detailed In-Depth Explanation:

When encountering issues where reports are incomplete or data appears to be missing after ETL (Extract, Transform, Load) processes, it's essential to assess the quality and structure of the data.Data profilingis the process of examining the data available in an existing data source and collecting statistics and information about that data. This practice helps in understanding the data's condition, identifying anomalies, and ensuring that the data conforms to the expected patterns.

Option A:Cross-validation

Rationale:Cross-validation is a statistical method used to estimate the skill of machine learning models. It is primarily used in predictive modeling to assess how the results of a statistical analysis will generalize to an independent dataset. While valuable in model evaluation, it doesn't address issues related to missing or incomplete data in ETL processes.

Option B:Data profiling

Rationale:Data profiling involves analyzing the data for accuracy and completeness. By performing data profiling, analysts can identify missing values, inconsistencies, and anomalies within the dataset. This process is crucial for diagnosing issues that arise during ETL operations, such as incomplete data loads or transformation errors.

This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:

=IF (condition, value_if_true, value_if_false)

The other data manipulation techniques are not examples of logical functions. Here is why:

WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:

AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:

BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is: