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| Section | Weight | Objectives |
|---|---|---|
| Visualization and Reporting | 23% | - Building dashboards and reports - Selecting appropriate visualizations and charts - Communicating insights and recommendations - Design principles and best practices |
| Data Concepts and Environments | 15% | - Data types, structures, and formats - Structured, semi-structured, and unstructured data - Data storage systems: databases, data marts, warehouses, data lakes - Data schemas, dimensions, and attributes |
| Data Governance, Quality and Controls | 14% | - Data governance frameworks and policies - Data quality standards and measurement - Data privacy, security, and compliance - Data lifecycle management and controls |
| Data Mining | 25% | - Querying and retrieving data - Data transformation, manipulation, and enrichment - Data acquisition and integration methods - Data profiling, cleansing, and validation |
| Data Analysis | 23% | - Analytical techniques: trend, performance, exploratory, comparative - Statistical methods: correlation, regression, hypothesis testing - Descriptive and inferential statistics - Interpreting results and identifying patterns/anomalies |
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NEW QUESTION # 175
A data analyst needs to create a data visualization that aids in un the cumulative impact of sequentially introduced values that are positive or negative. Which of the following data visualization methods should the analyst use?
Answer: B
Explanation:
Explanation
A waterfall chart is a type of data visualization that shows the cumulative impact of sequentially introduced values that are positive or negative. A waterfall chart typically has an initial value and a final value, with intermediate values shown as floating columns that either add to or subtract from the initial value. A waterfall chart can help visualize how different factors contribute to a net change in a value over time.
Therefore, the correct answer is B. References: [Waterfall Chart | Definition & Examples - Investopedia], [Waterfall Charts in Excel | How to Create Waterfall Chart in Excel?]
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NEW QUESTION # 176
Which of the following data sampling methods involves dividing a population into subgroups by similar characteristics?
Answer: D
NEW QUESTION # 177
A JSON file is an example of:
Answer: B
Explanation:
A JSON (JavaScript Object Notation) file is a text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa). JSON files are human- readable and can be interpreted by various programming languages, making them ideal for data interchange123.
JSON files typically contain an array of objects, with each object representing a record with a series of name- value pairs. This structured format is both easy to understand and write by humans and easy for machines to parse and generate4.
References:
* JSON's official definition and syntax rules1.
* A beginner's guide to JSON and its data types2.
* Understanding the JSON file format3.
* Detailed explanation of JSON as a structured data format4.
NEW QUESTION # 178
An analyst runs a report on a daily basis, and the number of datapoints must be validated before the data can be analyzed. The number of datapoints increases each day by approximately 20% of the total number from the day before. On a given day, the number of datapoints was 8,798. Which of the following should be the total number of datapoints on the next day?
Answer: B
NEW QUESTION # 179
Which of the following concepts should be applied if a data set with 40 fields needs to be pared down to 20 fields and contains similar data across multiple fields?
Answer: D
Explanation:
Consolidation is the process of combining multiple elements into a single, more effective or coherent whole.
In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.
References:
* Applied Dimensionality Reduction - 3 Techniques using Python1.
* Seven Techniques for Data Dimensionality Reduction2.
* Best practices when working with datasets3.
* Effectively Handling Large Datasets4.
NEW QUESTION # 180
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