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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Mining | 25% | - Data profiling, cleansing, and validation - Querying and retrieving data - Data transformation, manipulation, and enrichment - Data acquisition and integration methods |
| Topic 2: Data Governance, Quality and Controls | 14% | - Data lifecycle management and controls - Data quality standards and measurement - Data privacy, security, and compliance - Data governance frameworks and policies |
| Topic 3: Visualization and Reporting | 23% | - Selecting appropriate visualizations and charts - Building dashboards and reports - Communicating insights and recommendations - Design principles and best practices |
| Topic 4: Data Analysis | 23% | - Descriptive and inferential statistics - Analytical techniques: trend, performance, exploratory, comparative - Statistical methods: correlation, regression, hypothesis testing - Interpreting results and identifying patterns/anomalies |
| Topic 5: Data Concepts and Environments | 15% | - Structured, semi-structured, and unstructured data - Data storage systems: databases, data marts, warehouses, data lakes - Data types, structures, and formats - Data schemas, dimensions, and attributes |
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NEW QUESTION # 257
Which of the following best describe qualitative data? (Select two).
Answer: A,D
Explanation:
Qualitative data refers to non-numeric information that describes qualities or characteristics. It is often categorized based on attributes or properties and is typically descriptive in nature.
* Ordinal Data: This type of qualitative data represents categories with a meaningful order but without a consistent interval between them. Examples include rankings (e.g., customer satisfaction levels such as
'satisfied,' 'neutral,' 'dissatisfied') where the order matters, but the difference between ranks is not uniform.
* Nominal Data: This qualitative data type consists of categories without any inherent order. Examples include gender, race, or types of products. Each category is distinct, and there is no logical sequence.
In contrast, options A (Discrete) and D (Continuous) refer to quantitative data types:
* Discrete Data: Quantitative data that can take on only specific, distinct values (e.g., the number of students in a class).
* Continuous Data: Quantitative data that can take on any value within a range (e.g., height, weight).
Options C (Batch) and F (Real-time) pertain to data processing methods rather than data types.
Reference: CompTIA Data+ Certification Exam Objectives (DA0-001), Domain 1.2: Compare and contrast different data types.
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NEW QUESTION # 258
Which of the following best describes a 95% confidence interval?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth
A95% confidence intervalis a statistical range, derived from sample data, that is likely to contain the true population mean. It reflects the degree of uncertainty or certainty in a sampling method.
Option C:A set of ranges contains the population mean with 95% certainty.
Rationale:This statement accurately describes a 95% confidence interval. It means that if we were to take 100 different samples and compute a confidence interval for each sample, we would expect about 95 of the intervals to contain the true population mean.
Option A:There is a 95% probability that a sample is within one standard deviation of the mean.
Rationale:This statement confuses confidence intervals with standard deviation. A 95% confidence interval pertains to the range within which the population mean is expected to lie, not the dispersion of individual sample points around the mean.
Option B:A stated range may contain 95% of the population mean, 95% of the time.
Rationale:This statement is incorrect because the population mean is a single value, not something that can be 95%
NEW QUESTION # 259
Which of the following BEST describes standard deviation?
Answer: C
NEW QUESTION # 260
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?
Answer: C
Explanation:
Explanation
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
NEW QUESTION # 261
An analyst is preparing a report that contains weather data. The temperatures are shown in Fahrenheit. but they must be reported in Celsius. Which of the following should the analyst do to fix this issue?
Answer: D
Explanation:
Explanation
The analyst should rescale the data to fix this issue. Rescaling is a process of transforming data from one scale to another, such as changing the units of measurement. In this case, the analyst needs to rescale the temperatures from Fahrenheit to Celsius, which are two different scales for measuring temperature. To do this, the analyst can use the following formula:
Celsius = (Fahrenheit - 32) * 5/9
This formula converts each temperature value from Fahrenheit to Celsius by subtracting 32 and multiplying by
5/9. For example, if the temperature is 68Β°F, the rescaled value in Celsius is:
Celsius = (68 - 32) * 5/9 Celsius = 20Β°C
Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.
NEW QUESTION # 262
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