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| Section | Weight | Objectives |
|---|---|---|
| Data Governance, Quality, and Controls | 14% | - Summarize important data governance concepts - Compare and contrast types of data controls - Explain key aspects of data quality |
| Data Mining | 25% | - Explain data acquisition concepts - Explain common techniques for data manipulation and optimization - Identify common reasons for cleansing and profiling datasets - Given a scenario, execute techniques for data manipulation |
| Visualization | 23% | - Given a scenario, use appropriate methods for dashboard development - Compare and contrast types of reports - Given a scenario, translate business requirements to support data-driven decisions - Explain important aspects of a report that tell a data story - Given a scenario, apply the appropriate type of visualization |
| Data Concepts and Environments | 15% | - Identify basic concepts of data schemas and dimensions - Compare and contrast common data structures and file formats - Compare and contrast different data types |
| Data Analysis | 23% | - Explain the purpose of inferential statistical methods - Given a scenario, apply the appropriate descriptive statistical methods - Summarize types of analysis and key analysis techniques |
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NEW QUESTION # 273
An analyst is designing a dashboard to determine which site has the highest percentage of new customers. 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 to best display the data?
Answer: C
Explanation:
The best type of chart to display the data is A. Include a bar chart using the site and the percentage of new customers data.
A bar chart is a good choice for comparing categorical data with numerical data, such as the site and the percentage of new customers. A bar chart can show the relative differences between the sites and highlight the site with the highest percentage of new customers. A bar chart can also be easily labeled and formatted to make the data clear and understandable.
A line chart is not suitable for this data, because it is used to show trends or changes over time, which is not relevant for the site and the percentage of new customers data. A line chart would also be confusing and misleading, as it would imply a connection or correlation between the sites that does not exist.
A pie chart is also not a good choice for this data, because it is used to show the proportion of a whole, not the comparison of different categories. A pie chart would also be difficult to read and interpret, as it would require labels or legends to identify the sites and their percentages. A pie chart would also not be able to show the exact values of the percentages, only their relative sizes.
A scatter chart is another inappropriate option for this data, because it is used to show the relationship or correlation between two numerical variables, not between a categorical and a numerical variable. A scatter chart would also be cluttered and unclear, as it would plot each site as a point on a coordinate plane, without any labels or axes. A scatter chart would also not be able to show the differences or rankings between the sites and their percentages.
NEW QUESTION # 274
A financial institution is reporting on sales performance to a company at the account level. Due to the sensitive nature of the government the does il with, some account information is not shown. Which of the following fields should be masked?
Answer: A
Explanation:
Customer name is the field that should be masked, because it contains sensitive information that could identify the government accounts that the financial institution deals with. Masking is a technique that replaces or obscures sensitive data with dummy or random data, such as asterisks or hashes. Masking can help protect the privacy and security of the data, while still allowing for some analysis and reporting. Therefore, the correct answer is D. Reference: [Data Masking | Definition, Techniques & Examples - Talend], [Data masking - Wikipedia]
NEW QUESTION # 275
Which of the following variable name formats would be problematic if used in the majority of data software programs?
Answer: C
Explanation:
This is because First Name is a variable name format that would be problematic if used in most of the data software programs, such as Excel, SQL, or Python. This is because First Name contains a space between two words, which could cause confusion or errors in the data software programs, as they might interpret the space as a separator or a delimiter between two different variables or values, rather than as part of a single variable name. For example, in SQL, a space is used to separate keywords, clauses, or expressions in a statement, such as SELECT, FROM, WHERE, etc. Therefore, using First Name as a variable name in SQL could result in a syntax error or an unexpected result. The other variable name formats would not be problematic if used in most of the data software programs. Here is why:
First_Name_ is a variable name format that uses an underscore (_) to separate two words, which is a common and acceptable practice in most of the data software programs, as it helps to improve the readability and clarity of the variable name. For example, in Python, an underscore is used to follow the PEP 8 style guide for naming variables, which recommends using lowercase letters and underscores for multi-word variable names.
FirstName is a variable name format that uses camel case to separate two words, which is another common and acceptable practice in most of the data software programs, as it helps to reduce the length and complexity of the variable name. For example, in Excel, camel case is used to follow the VBA naming conventions for naming variables, which recommends using mixed case letters for multi-word variable names.
First_Name is a variable name format that also uses an underscore (_) to separate two words, which is also a common and acceptable practice in most of the data software programs, as it helps to improve the readability and clarity of the variable name. For example, in SQL, an underscore is used to follow the ANSI SQL naming standards for naming variables, which recommends using lowercase letters and underscores for multi-word variable names.
NEW QUESTION # 276
Mario works with a group of R programmers tasked with copying data from an accounting system into a data warehouse.
In what phase are the group's R skills most relevant?
Answer: B
NEW QUESTION # 277
An analysts building a monthly report for production and wants to ensure the audience is aware of its once-a-month cadence. Which of the following is the MOST important to convey that information?
Answer: B
Explanation:
Explanation
This is because the date of the dashboard build is the most important component to convey that information, which is the once-a-month cadence of the monthly report for production. The date of the dashboard build can convey that information by indicating when the dashboard was created or updated, as well as showing the frequency or interval of the dashboard creation or update. For example, the date of the dashboard build can convey that information by displaying a date format that includes the month and year, such as January 2020, February 2020, etc., or by displaying a text format that includes the word "monthly", such as Monthly Report for Production - January 2020, Monthly Report for Production - February 2020, etc. The other components are not the most important components to convey that information. Here is why:
The data refresh date is a component that indicates when the data on the dashboard was refreshed or retrieved from the source or system, such as a database, a cloud service, or a web application. The data refresh date does not convey that information, but rather conveys how current or up-to-date the data on the dashboard is.
A report summary is a component that provides an overview or a highlight of the main findings or insights from the dashboard, such as key metrics, indicators, or trends. A report summary does not convey that information, but rather conveys what the dashboard is about or what it shows.
Frequently asked questions is a component that provides answers or explanations to common or expected questions from the audience or users of the dashboard, such as how to use or interpret the dashboard, what are the assumptions or limitations of the dashboard, etc. Frequently asked questions does not convey that information, but rather conveys how to understand or interact with the dashboard.
NEW QUESTION # 278
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