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| Section | Weight | Objectives |
|---|---|---|
| Visualization | 23% | - Chart and dashboard creation - Visualization best practices - Tool-based visualization techniques |
| Data Mining | 25% | - Data acquisition and cleaning techniques - Data transformation and profiling - Pattern identification and extraction |
| Data Analysis | 23% | - Data interpretation and reporting - Trend and correlation analysis - Statistical methods and calculations |
| Data Governance, Quality, and Controls | 14% | - Data governance frameworks - Security, privacy, and compliance considerations - Data quality management |
| Data Concepts and Environments | 15% | - Data structures and file formats - Data concepts and terminology - Data sources and acquisition methods |
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NEW QUESTION # 88
The current date is July 14, 2020. A data analyst has been asked to create a report that shows the company's year-over-year Q2 2020 sales. Which of the following reports should the analyst compare?
Answer: A
Explanation:
To create a report that shows the company's year-over-year Q2 2020 sales, the analyst should compare the sales data from Q2 2020 and Q2 2019. Year-over-year (YoY) analysis is a method of comparing the performance of a business or a financial instrument over the same period in different years. It helps to identify trends, growth patterns, and seasonal fluctuations. Q2 refers to the second quarter of a year, which is usually from April to June. Therefore, the correct answer is C. Reference: YoY - Year over Year Analysis - Definition, & Examples, What is an Annual Sales Report: Definition, metrics, and tips - Snov.io
NEW QUESTION # 89
Which of the following best describes an exploratory analysis?
Answer: D
Explanation:
A) Involves the use of descriptive statistics to understand observations.
Exploratory data analysis (EDA) is a method of analyzing and investigating data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA involves the use of descriptive statistics, such as mean, median, mode, standard deviation, frequency, or percentage, to understand the distribution, central tendency, variability, and relationship of the data. EDA helps to see what the data can reveal beyond the formal modeling or hypothesis testing, and provides a better understanding of data set variables and the interactions between them1.
NEW QUESTION # 90
Given the image below:
The data should be cleaned because of the presence of:
Answer: B
Explanation:
The answer is A. Outlier.
Short explanation: An outlier is a data point that differs significantly from the rest of the data in a dataset. An outlier can indicate an error, an anomaly, or a rare event in the data.An outlier can affect the statistical analysis and visualization of the data, such as skewing the mean, variance, or distribution of the data.
Therefore, data should be cleaned to identify and remove or correct any outliers.
The image below shows a box plot graph with a vertical axis labeled "Customer Calls" and a horizontal axis labeled "Churn". The box plot is blue in color and the median value is around 2. There are 7 outliers above the box plot, ranging from 4 to 8.
image)
A box plot is a type of graph that can show the distribution of data values using five summary statistics:
minimum, maximum, median, first quartile, and third quartile. The box represents the interquartile range (IQR), which is the difference between the first and third quartiles. The median is shown as a line inside the box. The whiskers extend from the box to the minimum and maximum values, excluding any outliers.
Outliers are shown as dots or circles outside the whiskers.
In this graph, we can see that most of the customer calls are between 0 and 4, with a median of 2. However, there are 7 outliers that have more than 4 customer calls, up to 8. These outliers may indicate some customers who have more issues or complaints than others, or some errors or anomalies in the data collection or recording process. These outliers can affect the analysis and interpretation of the customer calls and churn relationship, such as making it seem that more customer calls lead to less churn, which may not be true for the majority of the customers. Therefore, data should be cleaned to investigate and handle these outliers appropriately.
NEW QUESTION # 91
A customer list from a financial services company is shown below:
A data analyst wants to create a likely-to-buy score on a scale from 0 to 100, based on an average of the three numerical variables: number of credit cards, age, and income. Which of the following should the analyst do to the variables to ensure they all have the same weight in the score calculation?
Answer: D
Explanation:
Normalizing the variables means scaling them to a common range, such as 0 to 1 or -1 to 1, so that they have the same weight in the score calculation. Recoding the variables means changing their values or categories, which would alter their meaning and distribution. Calculating the percentiles of the variables means ranking them relative to each other, which would not account for their actual magnitudes. Calculating the standard deviations of the variables means measuring their variability, which would not make them comparable.
References: CompTIA Data+ Certification Exam Objectives, page 10
NEW QUESTION # 92
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: C
Explanation:
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 # 93
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