CompTIA Updated DA0-001 Exam Questions and Answers by safiyyah

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]

his is because median is a type of statistical measure that represents the typical value or central tendency of a data set, which means that it divides the data set into two equal halves, such that half of the values are above it and half are below it. Median can be used to provide a measure that represents the typical income of suburban families in the United States, especially when the data set has a lot of outliers, which means that it has values that are unusually high or low compared to the rest of the data set. Median can provide a measure that represents the typical income of suburban families in the United States, because it is not affected or skewed by the outliers, as it only depends on the middle value or the middle two values of the data set, regardless of how extreme or distant the outliers are. For example, median can provide a measure that represents the typical income of suburban families in the United States, by finding the income value that splits the data set into two equal groups of families, such that 50% of the families have higher incomes and 50% have lower incomes. The other statistical measures are not the best measures to represent the typical income of suburban families in the United States. Here is why:

Mean is a type of statistical measure that represents the average value or central tendency of a data set, which means that it is the sum of all the values divided by the number of values. Mean is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is affected or skewed by the outliers, as it takes into account all the values in the data set, regardless of how extreme or distant they are. For example, mean can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is influenced by a few very high or very low incomes, which could make it higher or lower than most of the incomes in the data set.

Mode is a type of statistical measure that represents the most frequent value or mode of a data set, which means that it is the value that occurs most often in the data set. Mode is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is not representative or indicative of the central tendency or distribution of the data set, as it only depends on the count or occurrence of a single value or a few values in the data set, regardless of how common or rare they are. For example, mode can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is repeated more often than others, which could be an outlier or an anomaly in the data set.

Standard deviation is a type of statistical measure that represents the amount of dispersion or variation of a data set, which means that it quantifies how much the values in a data set vary or deviate from the mean or average of the data set. Standard deviation is not a measure that represents the typical income of suburban families in the United States, but rather a measure that describes the spread or distribution of their incomes, as well as identifies any outliers or extreme values in their incomes. For example, standard deviation can provide a measure that describes how diverse or homogeneous their incomes are, as well as how far their incomes are from their average income.

After writing the report, the most appropriate next step for the analyst is to complete a check for quality in the report. This involves reviewing the report for accuracy, clarity, completeness, consistency, and relevance. The analyst should ensure that the report addresses the director’s business questions and objectives, that the data and analysis are correct and reliable, that the tables and graphs are well-designed and easy to understand, that the descriptions and summary are concise and informative, and that there are no errors or inconsistencies in the report. A quality check will help the analyst to improve the presentation and communication of the report, as well as to avoid any misunderstandings or misinterpretations by the director1.

Explanation

Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses.