DA0-001 Exam Dumps - CompTIA Data+ Questions and Answers

Comprehensive and Detailed In-Depth Explanation:

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.

To create a report that shows the company’s year-over-year Q2 2020 sales, the analyst should compare the sales data from Q2 2020 and Q2 2019. Year-over-year (YoY) analysis is a method of comparing the performance of a business or a financial instrument over the same period in different years. It helps to identify trends, growth patterns, and seasonal fluctuations. Q2 refers to the second quarter of a year, which is usually from April to June. Therefore, the correct answer is C. References: YoY - Year over Year Analysis - Definition, Explanation & Examples, What is an Annual Sales Report: Definition, metrics, and tips - Snov.io

The mode is the value that appears most frequently in a data set. In the provided data set, the number 7 appears three times, which is more than any other number. Therefore, the mode of this data set is 7.

3 appears twice, but less frequently than 7.

5 and 10 each appear only once, so they cannot be the mode.

References:

Mode in Statistics - Definition and Examples1

Understanding Measures of Central Tendency2

Mode (statistics) - Wikipedia3

The correct answer is A. UNION ALL.

UNION ALL is a SQL statement that appends two tables that have the same number of columns and compatible data types.UNION ALL preserves all the rows from both tables, including any duplicates12

B. MERGE is not correct, because MERGE is a SQL statement that combines the data of two tables based on a common column.MERGE can perform insert, update, or delete operations on the target table depending on the matching or non-matching rows from the source table34

C. GROUP BY is not correct, because GROUP BY is a SQL clause that groups the rows of a table based on one or more columns.GROUP BY is often used with aggregate functions, such as SUM, AVG, COUNT, etc., to calculate summary statistics for each group56

D. JOIN is not correct, because JOIN is a SQL clause that combines the data of two tables based on a common column or condition. JOIN can produce different results depending on the type of join, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, etc.

The missing step in the Extract, Transform, Load (ETL) process is typically the cleaning step, which involves removing redundant data or deduplication. This step is crucial in the ETL process to ensure that the data loaded into the destination is accurate and not inflated by duplicate records. The other options, like validating data types and connecting to the data API, are important but do not fit into the standard ETL process steps as a cleaning operation. Normalizing the data is part of the 'Transform' step, which was already listed.

Data integrity is the dimension that measures the consistency and validity of data across different data sources. In this case, the data analyst wants to create one column that contains the customers’ birth dates, but the data is stored in different formats and locations in the sample schema. For example, some customers have their birth dates in the customer table, while others have their birth years in the sales table. To compile the data into one column, the data analyst needs to ensure that the data is consistent and valid across the tables. Therefore, data integrity is the best explanation for the reason for compilation. References: Data Quality Dimensions - DATAVERSITY, The 6 Data Quality Dimensions with Examples | Collibra

The term that best describes the action of changing the names of categories that you used for different types of books is recoding. Recoding is a process of transforming or modifying the values of a variable or a category to make them more meaningful, consistent, or accurate. For example, you can recode the names of book genres from “Fiction”, “Non-Fiction”, “Biography”, etc. to “FIC”, “NF”, “BIO”, etc. to make them shorter and easier to use. Reference: Recoding Data - SPSS Tutorials - LibGuides at Kent State University

Explanation

Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. The Snowflake data platform is not built on any existingdatabase technology or “big data” software platforms such as Hadoop.