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[Hardware] DA0-001 Updated Testkings - DA0-001 Exam Objectives

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【Hardware】 DA0-001 Updated Testkings - DA0-001 Exam Objectives

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CompTIA Data+ Certification Exam Sample Questions (Q151-Q156):NEW QUESTION # 151
A data analyst must separate the column shown below into multiple columns for each component of the name:

Which of the following data manipulation techniques should the analyst perform?
  • A. Imputing
  • B. Transposing
  • C. Parsing
  • D. Concatenating
Answer: C
Explanation:
Parsing is the data manipulation technique that should be used to separate the column into multiple columns for each component of the name. Parsing is the process of breaking down a string of text into smaller units, such as words, symbols, or numbers. Parsing can be used to extract specific information from a text column, such as names, addresses, phone numbers, etc. Parsing can also be used to split a text column into multiple columns based on a delimiter, such as a comma, space, or dash1. In this case, the analyst can use parsing to split the column by the comma delimiter and create three new columns: one for the last name, one for the first name, and one for the middle initial. This will make the data more organized and easier to analyze.

NEW QUESTION # 152
A data analyst has been asked to merge the tables below, first performing an INNER JOIN and then a LEFT JOIN:

Customer Table -
In-store Transactions -

Which of the following describes the number of rows of data that can be expected after performing both joins in the order stated, considering the customer table as the main table?
  • A. INNER: 15 rows; LEFT: 9 rows
  • B. INNER: 6 rows; LEFT: 9 rows
  • C. INNER: 9 rows; LEFT: 15 rows
  • D. INNER: 9 rows; LEFT: 6 rows
Answer: C
Explanation:
An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.
To perform an INNER JOIN, we can use the following SQL query:
SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 9 rows of data, as shown below:
customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09 To perform a LEFT JOIN, we can use the following SQL query:
SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 15 rows of data, as shown below:
customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.
Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

NEW QUESTION # 153
A data analyst has been asked to merge the tables below, first performing an INNER JOIN and then a LEFT JOIN:

Customer Table -
In-store Transactions -

Which of the following describes the number of rows of data that can be expected after performing both joins in the order stated, considering the customer table as the main table?
  • A. INNER: 15 rows; LEFT: 9 rows
  • B. INNER: 6 rows; LEFT: 9 rows
  • C. INNER: 9 rows; LEFT: 15 rows
  • D. INNER: 9 rows; LEFT: 6 rows
Answer: C
Explanation:
An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.
To perform an INNER JOIN, we can use the following SQL query:
SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 9 rows of data, as shown below:
customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M
| Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3
| 2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 |
4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09 To perform a LEFT JOIN, we can use the following SQL query:
SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 15 rows of data, as shown below:
customer_id|name|lastname|gender|marital_status|transaction_id|amount|date
1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02
2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04
3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06
5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08
7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.
Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.
Reference: SQL Joins - W3Schools

NEW QUESTION # 154
A data analyst has been asked to organize the table below in the following ways:
By sales from high to low -
By state in alphabetic order -

Which of the following functions will allow the data analyst to organize the table in this manner?
  • A. Filtering
  • B. Conditional formatting
  • C. Grouping
  • D. Sorting
Answer: D
Explanation:
Sorting is the function that will allow the data analyst to organize the table in the desired manner. Sorting means arranging the data in a specific order, such as ascending or descending, based on one or more criteria. Sorting can be applied to any column in the table, such as sales or state. Reference: CompTIA Data+ Certification Exam Objectives, page 11

NEW QUESTION # 155
Given the following data tables:

Which of the following MDM processes needs to take place FIRST?
  • A. Standardization of data field names
  • B. Consolidation of multiple data fields
  • C. Creation of a data dictionary
  • D. Compliance with regulations
Answer: C
Explanation:
This is because a data dictionary is a type of document that defines and describes the data elements, attributes, and relationships in a database or a data set. A data dictionary can be used to facilitate the MDM (Master Data Management) process, which is a process that aims to ensure the quality, consistency, and accuracy of the data across different sources and systems. By creating a data dictionary first, the analyst can establish a common understanding and standardization of the data field names, types, formats, and meanings, as well as identify any potential issues or conflicts in the data, such as missing values, duplicate values, or inconsistent values. The other MDM processes can take place after creating a data dictionary. Here is why:
Compliance with regulations is a type of MDM process that ensures that the data meets the legal and ethical requirements and standards of the industry or the organization. Compliance with regulations can take place after creating a data dictionary, because the data dictionary can help theanalyst to identify and apply the relevant rules and policies to the data, such as data privacy, security, or retention.
Standardization of data field names is a type of MDM process that ensures that the data field names are consistent and uniform across different sources and systems. Standardization of data field names can take place after creating a data dictionary, because the data dictionary can provide a reference and a guideline for naming and labeling the data fields, as well as resolving any discrepancies or ambiguities in the data field names.
Consolidation of multiple data fields is a type of MDM process that combines or merges the data fields from different sources or systems into a single source or system. Consolidation of multiple data fields can take place after creating a data dictionary because the data dictionary can help the analyst to map and match the data fields from different sources or systems based on their definitions and descriptions, as well as eliminating any redundant or duplicate data fields.

NEW QUESTION # 156
......
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