A data analyst is performing a data merge within a spreadsheet using the tables below: https://www.bing.com/images/blob?bcid=S1XCF9p02M4GjpbGxHj0lrIaj9sw.....4c

The analyst is attempting to pull the addresses from Table 2 into Table 1 using the last names and is receiving an error message. Which of the following steps can the analyst perform to fix the error?

Correct Answer:D
The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a successful data pull of addresses.
References: This answer is based on general data analytics practices and does not reference a specific document.

An analyst has been asked to validate data quality. Which of the following are the BEST reasons to validate data for quality control purposes? (Choose two.)

Correct Answer:B
Integrity and D. Consistency. This is because integrity and consistency are two of the best reasons to validate data for quality control purposes, which means to check and ensure that the data is accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for integrity and consistency, the analyst can prevent or correct any errors or issues in the data that could affect the validity or reliability of the analysis or the results. Here is what integrity and consistency mean in terms of data quality:
✑ Integrity refers to the completeness and validity of the data, which means that the data has no missing, incomplete, or invalid values that could compromise its meaning or usefulness. For example, validating data for integrity could involve checking for null values, outliers, or incorrect data types in the data set.
✑ Consistency refers to the uniformity and standardization of the data, which means
that the data follows a common format, structure, or rule across different sources or systems. For example, validating data for consistency could involve checking for spelling, punctuation, or capitalization errors in the data set.
The other reasons are not the best reasons to validate data for quality control purposes. Here is why:
✑ Retention refers to the storage and preservation of the data, which means that the data is kept and maintained in a secure and accessible way for future use or reference. Retention does not need to be validated for quality control purposes, because it does not affect the accuracy or reliability of the data itself.
✑ Transmission refers to the transfer and exchange of the data, which means that
the data is moved or shared between different sources or systems in a fast and efficient way. Transmission does not need to be validated for quality control purposes, because it does not affect the completeness or validity of the data itself.
✑ Encryption refers to the protection and security of the data, which means that the
data is encoded or scrambled in a way that prevents unauthorized access or use. Encryption does not need to be validated for quality control purposes, because it does not affect the uniformity or standardization of the data itself.
✑ Deletion refers to the removal and disposal of the data, which means that the data
is erased or destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for quality control purposes, because it does not affect the meaning or usefulness of the data itself.

A database consists of one fact table that is composed of multiple dimensions. Depending on the dimension, each one can be represented by a denormalized table or multiple normalized tables. This structure is an example of a:

Correct Answer:B
star schema is a type of database schema that consists of one fact table that is composed of multiple dimensions. A fact table contains quantitative measures or facts that are related to a specific event or transaction. A dimension table contains descriptive attributes or dimensions that provide context for the facts. A star schema is called so because it resembles a star, with the fact table at the center and the dimension tables radiating from it. A star schema is a type of dimensional schema, which is designed for data warehousing and analytical purposes. Other types of dimensional schemas include snowflake schema and galaxy schema. A snowflake schema is similar to a star schema, except that some or all of the dimension tables are normalized into multiple tables. A galaxy schema consists of multiple fact tables that share some common dimension tables. A transactional schema is a type of database schema that is designed for operational purposes, such as recording day- to-day transactions and activities. A transactional schema is usually normalized to reduce data redundancy and improve data integrity. A non-relational schema is a type of database schema that does not follow the relational model, which organizes data into tables with rows and columns. A non-relational schema can store data in various formats, such as documents, graphs, key-value pairs, etc.

An analyst has written the following code: SELECT *
FROM Cust_table
WHERE age > 60 AND City = "New York"
Which of the following criteria is the analyst retrieving?

Correct Answer:C
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.

During data cleansing, an analyst conducts measures of central tendency on a data set. Which of the following data is the analyst attempting to identify?

Correct Answer:C

A data analyst needs to create a weekly recurring report on sales performance and distribute it to all sales managers. Which of the following would be the BEST method to automate and ensure successful delivery for this task?

Correct Answer:A
Scheduled report delivery is a feature that allows a data analyst to automate the generation and distribution of a report at a specified time and frequency. This would be the best method to ensure that the sales managers receive the weekly report on sales performance without manual intervention. Subscription access delivery is a feature that allows users to subscribe to a report and access it on demand, but it does not automate the delivery. Printing out a copy or uploading the report to the server are manual methods that require more time and effort from the data analyst. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA