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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Mining | 25% | - Explain common techniques for data manipulation and optimization - Identify common reasons for cleansing and profiling datasets - Explain data acquisition concepts - Given a scenario, execute techniques for data manipulation |
| Topic 2: Visualization | 23% | - Given a scenario, translate business requirements to support data-driven decisions - Compare and contrast types of reports - Given a scenario, use appropriate methods for dashboard development - Explain important aspects of a report that tell a data story - Given a scenario, apply the appropriate type of visualization |
| Topic 3: Data Concepts and Environments | 15% | - Identify basic concepts of data schemas and dimensions - Compare and contrast different data types - Compare and contrast common data structures and file formats |
| Topic 4: Data Analysis | 23% | - Given a scenario, apply the appropriate descriptive statistical methods - Explain the purpose of inferential statistical methods - Summarize types of analysis and key analysis techniques |
| Topic 5: Data Governance, Quality, and Controls | 14% | - Summarize important data governance concepts - Explain key aspects of data quality - Compare and contrast types of data controls |
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74. Frage
A data analyst is designing a dashboard that will provide a story of sales and determine which site is providing the highest sales volume per customer. The analyst must choose an appropriate chart to include in the dashboard. The following data is available:
Which of the following types of charts should be considered?
Antwort: A
Begründung:
A scatter chart using sales volume and average sales per customer is the best type of chart to include in the dashboard. A scatter chart is a type of chart that displays the relationship between two numerical variables using dots or markers. A scatter chart can show how one variable affects another, how strong the correlation is between them, and how the data points are distributed. In this case, a scatter chart can show the story of sales and determine which site is providing the highest sales volume per customer by plotting the sales volume on the x-axis and the average sales per customer on the y-axis. Each dot on the chart will represent a site, and the analyst can easily compare the sites based on their position on the chart. A site with a high sales volume and a high average sales per customer will be in the upper right quadrant, indicating a high performance. A site with a low sales volume and a low average sales per customer will be in the lower left quadrant, indicating a low performance. A site with a high sales volume and a low average sales per customer will be in the lower right quadrant, indicating a high volume but low value. A site with a low sales volume and a high average sales per customer will be in the upper left quadrant, indicating a low volume but high value. A scatter chart can also show if there is a positive or negative correlation between the two variables, or if there is no correlation at all.
A positive correlation means that as one variable increases, so does the other. A negative correlation means that as one variable increases, the other decreases. No correlation means that there is no relationship between the two variables.
The other types of charts are not as suitable for this purpose. A line chart is a type of chart that displays the change of one or more variables over time using lines. A line chart can show trends, patterns, and fluctuations in the data. However, in this case, there is no time variable involved, so a line chart would not be appropriate.
A pie chart is a type of chart that displays the proportion of each category in a whole using slices of a circle.
A pie chart can show how each category contributes to the total and compare the relative sizes of each category. However, in this case, there are two numerical variables involved, so a pie chart would not be able to show their relationship. A column chart is a type of chart that displays the comparison of one or more variables across categories using vertical bars. A column chart can show how each category differs from each other and rank them by size. However, in this case, a column chart would not be able to show the relationship between sales volume and average sales per customer, as it would only show one variable for each site.
75. Frage
Which of the following should be accomplished NEXT after understanding a business requirement for a data analysis report?
Antwort: B
Begründung:
Exploratory data analysis (EDA) is a process of examining and summarizing a dataset using various techniques, such as descriptive statistics, visualizations, correlations, outliers detection, and hypothesis testing. EDA can help reveal the main characteristics, patterns, trends, and insights from the data, as well as identify any problems or issues with the data quality or structure. EDA is usually performed after understanding a business requirement for a data analysis report and before building a mock dashboard
/presentation layout. Therefore, the correct answer is B. References: [What is Exploratory Data Analysis? | Definition and Examples], [Exploratory Data Analysis in Python]
76. Frage
A data analyst is asked on the morning of April 9, 2020, to create a sales report that identifies sales year to date. The daily sales data is current through the end of the day. Which of the following date ranges should be on the report?
Antwort: C
Begründung:
This is because sales year to date refers to the sales that have occurred from the beginning of the current year until the current date. By creating a sales report that identifies sales year to date, the analyst can measure and compare the sales performance and progress of the current year. Since the analyst is asked to create the sales report on the morning of April 9, 2020, and the daily sales data is current through the end of the day, the date range that should be on the report is January 1, 2020 to April 9, 2020. The other date ranges are not correct for identifying sales year to date. Here is why:
January 1, 2020 to April 1, 2020 would not include the sales that occurred in the first eight days of April, which would underestimate the sales year to date.
January 1, 2020 to April 7, 2020 would not include the sales that occurred in the last two days of April, which would also underestimate the sales year to date.
January 1, 2020 to April 8, 2020 would not include the sales that occurred on April 9, which would also underestimate the sales year to date.
77. Frage
Given the following data tables:
Which of the following MDM processes needs to take place FIRST?
Antwort: C
Begründung:
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.
78. Frage
Given the following data:
Which of the following BEST describes the data set?
Antwort: D
Begründung:
Explanation
This is because inconsistency is a type of data quality issue that occurs when the data does not follow a common format, structure, or rule across different sources or systems, which can affect the efficiency and performance of the analysis or process. Inconsistency can be caused by having different spellings, punctuations, capitalizations, or abbreviations for the same or similar values in a data set, such as "M", "m",
"Male", or "male" for gender in this case. Inconsistency can be eliminated or reduced by using data cleansing techniques, such as standardizing or normalizing the data values. The other options are not correct descriptions of the data set. Here is why:
Data bias is a type of data quality issue that occurs when the data is not representative or proportional of the population or the parameter, which can affect the validity and reliability of the analysis or process.
Data bias can be caused by having a sample that is too small, too large, or too skewed for the population or the parameter, such as having only male customers for a product that targets both genders in this case.
Data bias can be eliminated or reduced by using sampling techniques, such as stratified or cluster sampling.
The data is incomplete is a type of data quality issue that occurs when the data is absent or missing in a data set, which can affect the accuracy and reliability of the analysis or process. The data is incomplete can be caused by various factors, such as human error, system error, or non-response. The data is incomplete can be addressed by using various methods, such as replacing or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression.
The data is outliers is a type of data quality issue that occurs when the data has values that are unusually high or low compared to the rest of the data set, which can affect the quality and validity of the analysis or process. The data is outliers can be caused by various factors, such as measurement error, natural variation, or extreme events. The data is outliers can be addressed by using various methods, such as removing or filtering out the outliers, or using robust statistics that are less sensitive to outliers, such as median, interquartile range, or box plot.
79. Frage
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