CompTIA DA0-001 Online Practice Questions and Exam Preparation

CompTIA DA0-001 Online Questions & Answers

• Question 281:

  Which of the following query statements would be used when filtering data in a relational database management system? (Select two).

  A. ORDER BY
  B. HAVING
  C. WHERE
  D. SELECT
  E. INSERT
  F. GROUP BY
  B. HAVING
  C. WHERE

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  Explanation

• Question 282:

  A company's marketing department wants to do a promotional campaign next month. A data analyst on the team has been asked to perform customer segmentation, looking at how recently a customer bought the product, at what frequency, and at what value.

  Which of the following types of analysis would this practice be considered?

  A. Prescriptive
  B. Trend
  C. Gap
  D. Custer
  D. Custer

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  Explanation

  Customer segmentation is a type of cluster analysis, which is a method of grouping data points based on their similarities or differences. Cluster analysis can help identify patterns and trends in the data, as well as target specific groups of

  customers for marketing purposes. One common technique for customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value. This technique assigns a score to each customer based on how recently they

  bought the product, how often they buy the product, and how much they spend on the product. These scores can then be used to create clusters of customers with different characteristics and preferences. Therefore, the correct answer is

  D.

  References:

  Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate Guide for Customer Segmentation

• Question 283:

  Which of the following is an example of PII?

  A. Age
  B. Name
  C. Ethnicity
  D. Gender
  B. Name

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  Explanation

  A name is an example of personally identifiable information (PII), which is any data that can be used to identify someone, either on its own or with other relevant data. A name is a direct identifier, which means that it can uniquely identify a person without the need for any additional information. For example, a full name, such as John Smith, can be used to distinguish or trace an individual's identity. Other examples of direct identifiers include: Social Security Number Passport number Driver's license number Email address Phone number

• Question 284:

  The total values in this month's revenue report are twice as much as last month's. Which of the following most likely occurred during the ETL process?

  A. The data cleansing processes failed to execute.
  B. The database connectivity failed.
  C. The report included the previous month's data.
  D. The data normalization processes failed.
  C. The report included the previous month's data.

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  Explanation

• Question 285:

  Which one of the following programming languages is specifically designed for use in analytics applications?

  A. Python.
  B. R
  C. C++
  D. Java.
  B. R

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  Explanation

• Question 286:

  Which of the following database schemas features normalized dimension tables?

  A. Flat
  B. Snowflake
  C. Hierarchical
  D. Star
  B. Snowflake

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  Explanation

  The correct answer is

  B. Snowflake.

  A snowflake schema is a type of database schema that features normalized dimension tables. A database schema is a way of organizing and structuring the data in a database. A dimension table is a table that contains descriptive attributes

  or characteristics of the data, such as product name, category, color, etc. A normalized table is a table that follows the rules of normalization, which is a process of reducing data redundancy and improving data integrity by organizing the data

  into smaller and simpler tables. A snowflake schema is a variation of the star schema, which is another type of database schema that features denormalized dimension tables. A denormalized table is a table that does not follow the rules of

  normalization, and may contain redundant or duplicated data. A star schema consists of a central fact table that contains quantitative measures or facts, such as sales amount, order quantity, etc., and several dimension tables that are directly

  connected to the fact table. A snowflake schema differs from a star schema in that the dimension tables are further split into sub-dimension tables, creating a snowflake-like shape.

  A snowflake schema has some advantages and disadvantages over a star schema. Some advantages are:

  It reduces the storage space required for the dimension tables, as it eliminates the redundant data.

  It improves the data quality and consistency, as it avoids the update anomalies that may occur in denormalized tables.

  It allows more detailed analysis and queries, as it provides more levels of dimensions.

  Some disadvantages are:

  It increases the complexity and number of joins required to retrieve the data from multiple tables, which may affect the query performance and speed. It reduces the readability and simplicity of the schema, as it has more tables and

  relationships to understand.

  It may require more maintenance and administration, as it has more tables to manage and updat.

• Question 287:

  A data set has the following values: Which of the following is the best reason for cleansing the data?

  

  A. Invalid data
  B. Redundant data
  C. Data outliers
  D. Missing data
  D. Missing data

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  Explanation

• Question 288:

  Which of the following is a process that is used during data integration to collect, blend, and load data?

  A. MDM
  B. ETL
  C. OLTP
  D. BI
  B. ETL

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  Explanation

  ETL is a process that is used during data integration to collect, blend, and load data. ETL stands for extract, transform, and load, which are the three main steps involved in moving data from different sources to a common destination, such as a data warehouse or a data lake. ETL helps to consolidate and standardize data for analysis and reporting purposes.

  References: CompTIA Data+ Certification Exam Objectives, page 12

• Question 289:

  Which of the following file formats is best suited to start exploratory analysis within statistical software?

  A. CSV
  B. XLSM
  C. XML
  D. JSON
  A. CSV

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  Explanation

• Question 290:

  Given the image below:

  

  The data should be cleaned because of the presence of:

  A. outlier
  B. non-parametric data.
  C. multicollinearity.
  D. invalid data.
  A. outlier

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  Explanation

  The answer is A. Outlier. Short 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.