CompTIA DA0-001 Online Practice Questions and Exam Preparation

CompTIA DA0-001 Online Questions & Answers

• Question 81:

  A database administrator needs to increase performance on a large dimension table. Which of the following is the best way to accomplish this task?

  A. Sampling
  B. Partitioning
  C. Windowing
  D. Sorting
  B. Partitioning

  ---

  Explanation

• Question 82:

  A data set was recorded using multimedia technology. Which of the following is a necessary step on the way to interpretation?

  A. Structural equation modeling
  B. Transcription
  C. Sequential analysis
  D. Sampling
  B. Transcription

  ---

  Explanation

  The correct answer is

  B. Transcription.

  Transcription is a necessary step on the way to interpretation when a data set was recorded using multimedia technology. Multimedia technology refers to the use of various forms of media, such as audio, video, images, and text, to capture

  and present information. Transcription is the process of converting multimedia data into written or textual form, which can then be analyzed using various methods and tools. Transcription can help to make the data more accessible,

  searchable, and manageable, as well as to preserve the data for future use.

  Structural equation modeling is not correct, because it is a statistical technique that tests the causal relationships between multiple variables using observed and latent variables. Structural equation modeling is not a necessary step on the

  way to interpretation, but rather an optional method that can be applied to certain types of data. Sequential analysis is not correct, because it is a method of analyzing the order and timing of events or behaviors in a data set. Sequential

  analysis is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data. Sampling is not correct, because it is the process of selecting a subset of data from a larger population for

  analysis. Sampling is not a necessary step on the way to interpretation, but rather a preliminary step that can be done before collecting or analyzing the data.

• Question 83:

  Which of the following is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language?

  A. SAS
  B. Microsoft Power BI
  C. IBM SPSS
  D. Python
  D. Python

  ---

  Explanation

  Python is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language. Python has a simple and expressive syntax that makes it easy to read and write code. Python also has a rich set

  of libraries and frameworks that support various tasks and applications in data analytics, such as data manipulation, visualization, machine learning, natural language processing, web scraping, and more. Some examples of popular Python

  libraries for data analytics are pandas, numpy, matplotlib, seaborn, scikit-learn, nltk, and beautifulsoup. Python is different from other data analytics tools that are not programming languages but rather software applications or platforms that

  provide graphical user interfaces (GUIs) for data analysis and visualization. Some examples of these tools are SAS, Microsoft Power BI, IBM SPSS. Therefore, the correct answer is

  D.

  References:

  [What is Python? | Definition and Examples], [Python Libraries for Data Science]

• Question 84:

  Which of the following would be used to store unstructured data from different sources?

  A. A data lake
  B. A database management system
  C. A database
  D. A data warehouse
  A. A data lake

  ---

  Explanation

  This is because a data lake is a type of storage system that stores unstructured data from different sources, such as text, images, audio, video, etc. A data lake can be used to store unstructured data from different sources by using a schema-on- read approach, which means that it does not impose any structure or format on the data when it is stored, but rather applies it when it is read or accessed. A data lake can also be used to store unstructured data from different sources by using a distributed file system, such as Hadoop, which means that it can store large volumes and varieties of data across multiple servers or nodes. The other storage systems are not used to store unstructured data from different sources. Here is why:

  A database management system is a type of software application that manages and controls databases, which are collections of structured or semi-structured data that are organized into tables, rows, and columns. A database management system is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a schema- on-write approach, which means that it imposes a structure or format on the data when it is stored, and requires it to follow certain rules and constraints, such as primary keys, foreign keys, or referential integrity. A database is a type of storage system that stores structured or semi-structured data that are organized into tables, rows, and columns. A database is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a relational model, which means that it establishes and maintains relationships between different tables based on common columns or keys. A database can also be used to store structured or semi-structured data from specific sources by using a query language, such as SQL, which means that it can access and manipulate the data using statements or commands. A data warehouse is a type of storage system that stores structured or semi- structured data that are integrated and aggregated from different sources or systems, such as databases, cloud services, or web applications. A data warehouse is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from various sources by using an ETL process, which means that it extracts, transforms, and loads the data into a common format, structure, or schema. A data warehouse can also be used to store structured or semi-structured data from various sources by using an OLAP model, which means that it supports online analytical processing of the data using multidimensional cubes or queries.

• Question 85:

  What analytics suite is offered by Microsoft and directly integrates with SQL Server Databases?

  A. Qlik.
  B. Power BI.
  C. Domo.
  D. Dataroma.
  B. Power BI.

  ---

  Explanation

  Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet or a collection of cloud-based and on- premises hybrid data warehouses.

• Question 86:

  During data profiling, an analyst decides to recode the status column in the following data set:

  

  Which of the following data concerns explains why the analyst wants to take this action?

  A. Redundancy
  B. Duplication
  C. Invalidity
  D. Inconsistency
  D. Inconsistency

  ---

  Explanation

  The `Status' column in the dataset shows different terms such as "yes", "completed", "done", and "Y" that likely represent the same outcome - that a task has been completed. This variation in terms leads to inconsistency within the data. Data

  profiling aims to ensure that data is consistent, among other quality metrics, to facilitate accurate analysis and reporting. By recoding the `Status' column, the analyst seeks to address this inconsistency, ensuring that all entries indicating

  completion are represented uniformly. This enhances the data quality and usability for subsequent data analysis tasks.

  References:

  The action of recoding is taken to standardize the data entries and eliminate inconsistencies, which is crucial for maintaining data integrity and ensuring reliable data analysis.

• Question 87:

  A data analyst wants to create "Income Categories" that would be calculated based on the existing variable "Income". The "Income Categories" would be as follows:

  Income category 1: less than $1. Income category 2: more than $1 and less than $20,000. Income category 3: more than $20,001 and less than $40,000. Income category 4: more than $40,001. Which of the following data manipulation techniques should the data analyst use to create "Income Categories"?

  A. Data merge
  B. Derived variables
  C. Data blending
  D. Data append
  B. Derived variables

  ---

  Explanation

  The correct answer is B: Derived variables Derived variables are variables that you create by calculating or categorizing variables that already exist in your data set. Data merge is incorrect. Data merging is the process of combining two or

  more data sets into a single data set. Data blending is incorrect. Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis.

  Data append is incorrect. A data append is a process that involves adding new data elements to an existing database.

• Question 88:

  A table in a hospital database has a column for patient height in inches and a column for patient height in centimeters. This is an example of:

  A. dependent data.
  B. duplicate data.
  C. invalid data
  D. redundant data
  D. redundant data

  ---

  Explanation

  This is because redundant data is a type of data that is unnecessary or irrelevant for the analysis or purpose, which can affect the efficiency and performance of the analysis or process. Redundant data can be caused by having multiple data fields that store the same or similar information, such as patient height in inches and patient height in centimeters in this case. Redundant data can be eliminated or reduced by using data cleansing techniques, such as removing or merging the redundant data fields. The other types of data are not examples of data that is unnecessary or irrelevant for the analysis or purpose. Here is what they mean in terms of data quality:

  Dependent data is a type of data that relies on or is influenced by another data field or value, such as a formula or a calculation that uses other data fields or values as inputs or outputs. Dependent data can be useful or important for the analysis or purpose, as it can provide additional information or insights based on the existing data. Duplicate data is a type of data that is repeated or copied in a data set, which can affect the quality and validity of the analysis or process. Duplicate data can be caused by having multiple records or rows that have the same or similar values for one or more data fields or columns, such as customer ID or order ID. Duplicate data can be eliminated or reduced by using data cleansing techniques, such as removing or filtering out the duplicate records or rows. Invalid data is a type of data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis or process. Invalid data can be caused by having values that do not match the expected format, type, range, or rule for a data field or column, such as an email address that does not have an @ symbol or a date that does not follow the YYYY-MM-DD format. Invalid data can be eliminated or reduced by using data cleansing techniques, such as validating or correcting the invalid values.

• Question 89:

  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?

  A. Normalize the data.
  B. Standardize the data.
  C. Rescale the data.
  D. Aggregate the data.
  C. Rescale the data.

  ---

  Explanation

  The analyst should rescale the data to fix this issue. Rescaling is the 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 68

• Question 90:

  An analyst develops an IT document and needs to describe the technical terms used in the document. Which of the following is where the analyst should include descriptions of the technical terms?

  A. Glossary
  B. System diagram
  C. User requirements
  D. Index
  A. Glossary

  ---

  Explanation

  In technical documentation, a glossary is the designated section where definitions for technical terms are provided. It serves as a reference point for readers to understand specialized or uncommon words used within the document. Including

  descriptions of technical terms in a glossary ensures that readers have a consistent resource to refer to, which can improve comprehension and reduce misunderstandings. A system diagram (Option B) is a visual representation of the

  system's components and their interactions, not a place for defining terms. User requirements (Option C) outline what end-users expect from the system, and an index (Option D) is an alphabetical list of topics covered in the document,

  usually with page numbers, but not definitions.

  References:

  Creating effective technical documentation1. Best practices when writing technical descriptions3.