Explanation
Surveys sent to 100 randomly selected homes that are reflective of the population. This is because a random sample is a type of sample that is selected by using a random method, such as a lottery or a computer-generated number, which ensures that every element in the population has an equal chance of being selected. A random sample can result in a representative sample, which means that the sample reflects the characteristics and diversity of the population. By sending surveys to 100 randomly selected homes that are reflective of the population, the analyst can ensure that the sample is representative of the county's households and their income levels. The other sampling methods are not likely to result in a representative sample. Here is why:
A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m. would result in a biased sample, which means that the sample favors or excludes certain groups or elements in the population.
By conducting the survey only between 2:00 p.m. and 3:00 p.m., the analyst would miss out on people who are not available or reachable at that time, such as those who are working or sleeping. This could affect the representativeness and generalizability of the sample.
A systematic survey that is sent to 100 single-family homes in the county would result in an unrepresentative sample, which means that the sample does not reflect the characteristics and diversity of the population. By sending surveys only to single-family homes, the analyst would ignore other types of households, such as apartments, condos, or mobile homes. This could affect the accuracy and reliability of the sample.
Surveys sent to ten randomly selected homes within 5mi (8km) of the county's office would result in a small sample, which means that the sample size is too low to capture the variability and diversity of the population.
By sending surveys only to ten homes within a limited area, the analyst would miss out on many households that are located in different parts of the county. This could affect the precision and confidence of the sample.

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. References: CompTIA Data+ Certification Exam Objectives, page 11

Data encryption is a way of translating data from plaintext (unencrypted) to ciphertext (encrypted). Users can access encrypted data with an encryption key and decrypted data with a decryption key.

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.

The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation.
Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a better understanding of the data and its quality, as well as prepare the data for further analysis.