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| Section | Weight | Objectives |
|---|---|---|
| Data Concepts and Environments | 15% | - Data schemas, dimensions, and attributes - Data storage systems: databases, data marts, warehouses, data lakes - Structured, semi-structured, and unstructured data - Data types, structures, and formats |
| Data Mining | 25% | - Data transformation, manipulation, and enrichment - Data profiling, cleansing, and validation - Data acquisition and integration methods - Querying and retrieving data |
| Data Analysis | 23% | - Statistical methods: correlation, regression, hypothesis testing - Analytical techniques: trend, performance, exploratory, comparative - Descriptive and inferential statistics - Interpreting results and identifying patterns/anomalies |
| Data Governance, Quality and Controls | 14% | - Data quality standards and measurement - Data lifecycle management and controls - Data privacy, security, and compliance - Data governance frameworks and policies |
| Visualization and Reporting | 23% | - Design principles and best practices - Building dashboards and reports - Communicating insights and recommendations - Selecting appropriate visualizations and charts |
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NEW QUESTION # 34
What type of regulation is the Payment Card Industry Data Security Standard (PCIDSS)?
Answer: D
NEW QUESTION # 35
Which of the following data cleansing issues will be fixed when a DISTINCT function is applied?
Answer: D
Explanation:
Explanation
This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:
The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:
Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.
Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.
Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.
NEW QUESTION # 36
The number of phone calls that the call center receives in a day is an example of:
Answer: D
Explanation:
Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the number of phone calls that a call center receives in a day are all examples of discrete data.
Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is D. References: [Discrete vs Continuous Data:
Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]
NEW QUESTION # 37
A database consists of one fact table that is composed of multiple dimensions. Depending on the dimension, each one can be represented by a denormalized table or multiple normalized tables. This structure is an example of a:
Answer: D
Explanation:
star schema is a type of database schema that consists of one fact table that is composed of multiple dimensions. A fact table contains quantitative measures or facts that are related to a specific event or transaction. A dimension table contains descriptive attributes or dimensions that provide context for the facts. A star schema is called so because it resembles a star, with the fact table at the center and the dimension tables radiating from it. A star schema is a type of dimensional schema, which is designed for data warehousing and analytical purposes. Other types of dimensional schemas include snowflake schema and galaxy schema. A snowflake schema is similar to a star schema, except that some or all of the dimension tables are normalized into multiple tables. A galaxy schema consists of multiple fact tables that share some common dimension tables. A transactional schema is a type of database schema that is designed for operational purposes, such as recording day-to-day transactions and activities. A transactional schema is usually normalized to reduce data redundancy and improve data integrity. A non-relational schema is a type of database schema that does not follow the relational model, which organizes data into tables with rows and columns. A non-relational schema can store data in various formats, such as documents, graphs, key-value pairs, etc.
NEW QUESTION # 38
An analyst needs to join two tables of data together for analysis. All the names and cities in the first table should be joined with the corresponding ages in the second table, if applicable.
Which of the following is the correct join the analyst should complete. and how many total rows will be in one table?
Answer: D
Explanation:
The correct join the analyst should complete is B. LEFT JOIN, four rows.
A LEFT JOIN is a type of SQL join that returns all the rows from the left table, and the matched rows from the right table. If there is no match, the right table will have null values. A LEFT JOIN is useful when we want to preserve the data from the left table, even if there is no corresponding data in the right table1 Using the example tables, a LEFT JOIN query would look like this:
SELECT t1.Name, t1.City, t2.Age FROM Table1 t1 LEFT JOIN Table2 t2 ON t1.Name = t2.Name; The result of this query would be:
Name City Age Jane Smith Detroit NULL John Smith Dallas 34 Candace Johnson Atlanta 45 Kyle Jacobs Chicago 39 As you can see, the query returns four rows, one for each name in Table1. The name John Smith appears twice in Table2, but only one of them is matched with the name in Table1. The name Jane Smith does not appear in Table2, so the age column has a null value for that row.
NEW QUESTION # 39
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