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Question 1:
Given the following graph: Which of the following summary statements upholds integrity in data reporting?
A. Sales are approximately equal for Product A and Product B across all strategies.
B. Strategy 4 provides the best sales in comparison to other strategies.
C. While Strategy 2 does not result in the highest sales of Product D, over all products it appears to be the most effective.
D. Product D should be promoted more than the other products in all strategies.
Correct Answer: B
Explanation: Strategy 4 provides the best sales in comparison to other strategies. This is because the total sales for Strategy 4 are the highest among all the strategies, as shown by the black line. The other statements are not accurate or do not uphold integrity in data reporting. Here is why:
Statement A is false because sales are not approximately equal for Product A and Product B across all strategies. For example, in Strategy 1, Product A has more sales than Product B, while in Strategy 3, Product B has more sales than Product A. Statement C is misleading because it does not account for the difference in scale between the products. While Strategy 2 has the highest total sales among all products, it does not necessarily mean that it is the most effective for each product. For instance, Product D has very low sales in Strategy 2 compared to other strategies. Statement D is biased because it does not provide any evidence or justification for why Product D should be promoted more than the other products in all strategies. It also ignores the fact that Product D has the lowest sales among all products in most of the strategies.
Question 2:
Which of the following describes the method of sampling in which elements of data are selected randomly from each of the small subgroups within a population?
A. Simple random
B. Cluster
C. Systematic
D. Stratified
Correct Answer: D
Explanation: This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:
Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number. Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list. Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element. Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.
Question 3:
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: 6 rows; LEFT: 9 rows
B. INNER: 9 rows; LEFT: 6 rows
C. INNER: 9 rows; LEFT: 15 rows
D. INNER: 15 rows; LEFT: 9 rows
Correct Answer: C
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|202001-04 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
Question 4:
Given the table below: Which of the following variable types BEST describes the “Year” column?
A. Numeric
B. Date
C. Alphanumeric
D. Text
Correct Answer: B
Explanation: This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates,
birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the “Year”
column. Here is why:
Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example,
numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.
Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as
names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.
Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words. Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text
variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.
Question 5:
Which of the following are reasons to create and maintain a data dictionary? (Choose two.)
A. To improve data acquisition
B. To remember specifics about data fields
C. To specify user groups for databases
D. To provide continuity through personnel turnover
E. To confine breaches of PHI data
F. To reduce processing power requirements
Correct Answer: AB
Explanation: The reasons to create and maintain a data dictionary are to improve data acquisition and to remember specifics about data fields. A data dictionary is a document or a database that describes the structure, meaning, and usage of the data elements in a data source or a database. A data dictionary can help to improve data acquisition by providing clear and consistent definitions, rules, and standards for the data collection process. A data dictionary can also help to remember specifics about data fields by providing information such as data type, format, length, range, default value, constraints, relationships, etc. The other options are not reasons to create and maintain a data dictionary, as they are related to other aspects of data management or security. A data dictionary does not specify user groups for databases, as this is a function of access control or authorization. A data dictionary does not provide continuity through personnel turnover, as this is a function of documentation or knowledge transfer. A data dictionary does not confine breaches of PHI data, as this is a function of encryption or anonymization. A data dictionary does not reduce processing power requirements, as this is a function of optimization or compression. Reference: [What is a Data Dictionary? – DataCamp]
Question 6:
A data analyst is working with a team to create a dashboard for a client who requires on- demand access. Which of the following is the best delivery method to support the clients\’ requirement?
A. Email
B. Scheduled
C. Subscription
D. Static
Correct Answer: C
The best delivery method to support the client\’s requirement is C. Subscription. Short explanation: A subscription is a delivery method that allows the client to access the dashboard on-demand, whenever they need it. A subscription can be set up by the data analyst or the client themselves, and it can be configured to send an email notification when the dashboard is updated or refreshed. A subscription also allows the client to view the dashboard online or download it as a file format of their choice12 A. Email is not the best delivery method because it does not allow the client to access the dashboard on-demand. Email deliveries are sent at a fixed time or frequency, and they may not reflect the latest data or changes in the dashboard. Email deliveries also have limitations on the file size and format of the dashboard attachments1 B. Scheduled is not the best delivery method because it does not allow the client to access the dashboard on-demand. Scheduled deliveries are similar to email deliveries, except that they are triggered by a specific event or condition, such as a data update or a threshold value. Scheduled deliveries also have the same limitations as email deliveries on the file size and format of the dashboard attachments1
D. Static is not the best delivery method because it does not allow the client to access the dashboard on-demand. Static deliveries are one-time deliveries that are manually generated by the data analyst or the client. Static deliveries do not update or refresh automatically, and they may become outdated or irrelevant over time. Static deliveries also have limitations on the file size and format of the dashboard files3
Question 7:
What would be an example of an acceptable form of primary identification for the Data+ exam?
A. Passport.
B. School ID card.
C. Employee ID card.
D. Credit card with photo and signature.
Correct Answer: A
Question 8:
Which of the following best describes the process of examining data for statistics and information about the data?
A. Cleansing
B. search
C. Profiling
D. Governance
Correct Answer: C
Explanation: Data profiling is the process of examining data for statistics and information about the data, such as the structure, format, quality, and content of the data. Data profiling can help to understand the characteristics, patterns, relationships, and anomalies of the data, as well as to identify and resolve any errors, inconsistencies, or missing values in the data. Data profiling can be done using various tools and methods, such as spreadsheets, databases, or programming languages12.
Question 9:
Given the following data tables:
Which of the following MDM processes needs to take place FIRST?
A. Creation of a data dictionary
B. Compliance with regulations
C. Standardization of data field names
D. Consolidation of multiple data fields
Correct Answer: A
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 the analyst 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.