A military commander would like to see the health scorecards of the troops daily and filter them based on gender and rank. Considering this data is PHI, which of the following would be the best way for the commander to view the information?
A. An emailed report
B. A password-protected dashboard
C. A daily printout of a report
D. A cloud-hosted spreadsheet
Correct Answer: B
A password-protected dashboard is a type of web-based application that can display the health scorecards of the troops in a secure and interactive way. A password-protected dashboard can provide the following benefits for the commander: It can protect the PHI data from unauthorized access or disclosure by requiring a valid username and password to log in. This can ensure that only the commander and other authorized personnel can view the information12 It can allow the commander to filter the data based on gender and rank by using drop-down menus, sliders, checkboxes, or other controls. This can enable the commander to customize the view and focus on the relevant data13 It can update the data daily by connecting to a data source that refreshes automatically or on demand. This can ensure that the commander always sees the latest and most accurate information14 It can present the data in a visual and intuitive way by using charts, graphs, tables, or other elements. This can help the commander to understand and analyze the data more easily and effectively1
Question 192:
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.
Question 193:
A customer list from a financial services company is shown below:
A data analyst wants to create a likely-to-buy score on a scale from 0 to 100, based on an average of the three numerical variables: number of credit cards, age, and income. Which of the following should the analyst do to the variables to ensure they all have the same weight in the score calculation?
A. Recode the variables.
B. Calculate the percentiles of the variables.
C. Calculate the standard deviations of the variables.
D. Normalize the variables.
Correct Answer: D
Normalizing the variables means scaling them to a common range, such as 0 to 1 or -1 to 1, so that they have the same weight in the score calculation. Recoding the variables means changing their values or categories, which would alter their meaning and distribution. Calculating the percentiles of the variables means ranking them relative to each other, which would not account for their actual magnitudes. Calculating the standard deviations of the variables means measuring their variability, which would not make them comparable. References: CompTIA Data+ Certification Exam Objectives, page 10
Question 194:
An analyst must obtain the average daily sales for the following week: Which of the following must the analyst perform to obtain this value?
A. Data normalization
B. Data append
C. Data aggregation
D. Data blending
Correct Answer: C
Explanation: Data aggregation is the process of compiling data from multiple sources and summarizing it into a single dataset. Data aggregation can be used to calculate statistics, such as averages, sums, counts, or percentages. In this case, the analyst must obtain the average daily sales for the following week, which is a statistic that can be calculated by aggregating the sales data from each day and dividing by the number of days. Data aggregation can be done using various tools and methods, such as spreadsheets, databases, or programming languages.
Question 195:
Which of the following query optimization techniques involves examining only the data that is needed for a particular task?
A. Making a temporary table
B. Creating a flat file
C. Indexing documents
D. Creating an execution plan
Correct Answer: C
Explanation: The correct answer is C. Indexing documents. Indexing documents is a query optimization technique that involves creating a data structure that allows faster access to the data in the documents. Indexing documents can reduce the amount of data that needs to be scanned for a particular query, thus improving the performance and efficiency of the query. Indexing documents can also help with searching, sorting, filtering, and aggregating the data in the documents12
Question 196:
Jenny wants to study the academic performance of undergraduate sophomores and wants to determine the average grade point average at different points during an academic year.
What best describes the data set she needs?
A. Sample.
B. Observation.
C. Variable.
D. Population.
Correct Answer: A
Correct answer A. Sample.
Jenny does not have data for the entire population of all undergraduate sophomores. While a specific grade point average is an observation of variable, jenny needs sample data.
Question 197:
A financial analyst is creating a daily billing report for a company. One night, the company's data warehouse did not update the data, which caused the data to be reported incorrectly the next day. Which of the following documentation elements should the analyst add to catch this error?
A. Version number
B. Data refresh
C. Frequently asked questions tab
D. Summary
Correct Answer: B
A data refresh is a documentation element that indicates when the data was last updated or refreshed from the source. A data refresh can help the analyst to catch the error of the data warehouse not updating the data, as it will show a discrepancy between the expected and actual date of the data update. A data refresh can also help the users of the report to verify the timeliness and accuracy of the data, and to avoid making decisions based on outdated or incorrect data
Question 198:
Which of the following is the correct data type for text?
A. Boolean
B. String
C. Integer
D. Float
Correct Answer: B
Explanation: A string is a data type that represents a sequence of characters, such as text, symbols, numbers, or punctuation marks. Strings are enclosed in quotation marks, such as "Hello", "123", or "!@#". Strings can be manipulated, concatenated, sliced, indexed, formatted, and searched using various methods and functions. A string is different from other data types, such as boolean, integer, or float, which represent logical values (true or false), whole numbers, or decimal numbers respectively. Therefore, the correct answer is B. References: What is a String? | Definition and Examples, Python String Methods
Question 199:
Joseph is interpreting a left skewed distribution of test scores. Joe scored at the mean, Alfonso scored at the median, and gaby scored and the end of the tail.
Who had the highest score?
A. Joseph
B. Joe
C. Alfonso
D. Gaby
Correct Answer: C
Alfonso had the highest score. A left skewed distribution is a distribution where the tail is longer on the left side than on the right side, meaning that most of the values are clustered on the right side and there are some outliers on the left side.
In a left skewed distribution, the mean is less than the median, which is less than the mode. Therefore, Joseph, who scored at the mean, had the lowest score, Gaby, who scored at the end of the tail, had the second lowest score, and
Alfonso, who scored at the median, had the highest score.
Reference: Skewness - Statistics How To
Question 200:
Which of the following is an example of a discrete data type?
A. 8in (20cm)
B. 5 kids
C. 2.5mi (4km)
D. 10.7lbs (4.9kg)
Correct Answer: B
Explanation: A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy
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