Pass the CompTIA CompTIA Data+ DA0-001 Questions and answers with CertsForce

 Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:

Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.

Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.

Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn’t assume a normal distribution. Duplicate data © and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.

Adding more data reduces statistical errors by improving the reliability and accuracy of statistical measures. A larger sample size helps balance variations and minimizes the impact of outliers​.

Consolidation is the process of combining multiple elements into a single, more effective or coherent whole. In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.

Data consistency refers to ensuring that all values in a dataset follow a uniform format and structure. In this dataset:

One age value is recorded as "65F," which includes anextra character (F)that makes it inconsistent with other numeric age values.

The date formats are inconsistent (6/1/22 vs. 6/19/2022), but this is unrelated to the age issue.

Option A (Completeness):Incorrect. There are no missing values in the age column.

Option B (Consistency):Correct.The age data should be consistently formatted asnumeric values only, without extra characters like "F."

Option C (Accuracy):Incorrect. There is no evidence that theactual age valuesare incorrect, only that they are formatted inconsistently.

Option D (Manipulation):Incorrect. There is no indication that the data was intentionally altered for deception.

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.

When incorporating new customer information from a survey into an existing customer contacts database, the appropriate data manipulation technique is:

Option B:Append

Rationale:Appending involves adding new records to the end of an existing dataset without altering the existing data. In this scenario, the analyst adds the new customer information as additional records in the database.

Option A:Join

Rationale:Joining combines data from two or more tables based on a related column, creating a new dataset with combined information. This is not necessary when simply adding new records.

Option C:Transform

Rationale:Transformation involves changing the format, structure, or values of data. While important in data processing, it doesn't pertain to adding new records.

Option D:Blend

Rationale:Data blending combines data from different sources into a single dataset, often used in analysis rather than updating a database with new records.