CompTIA CompTIA Certifications DA0-001 Questions & Answers

• Question 51:

  An analyst is required to run a text analysis of data that is found in articles from a digital news outlet. Which of the following would be the BEST technique for the analyst to apply to acquire the data?

  A. Web scraping

  B. Sampling

  C. Data wrangling

  D. ETL

  Correct Answer: A

  Explanation: This is because web scraping is a technique that allows the analyst to extract data from web pages, such as articles from a digital news outlet. Web scraping can be done using various tools and methods, such as Python

  libraries, browser extensions, or online services. The other techniques are not suitable for acquiring data from web pages.

  Here is why:

  Sampling is a technique that involves selecting a subset of data from a larger population, usually for statistical analysis or testing purposes. Sampling does not help the analyst to acquire data from web pages, but rather to reduce the amount

  of data to be analyzed. Data wrangling is a technique that involves transforming and cleaning data to make it suitable for analysis or visualization. Data wrangling does not help the analyst to acquire data from web pages, but rather to improve

  the quality and usability of the data. ETL stands for Extract, Transform, and Load, which is a process that involves moving data from one or more sources to a destination, such as a data warehouse or a database. ETL does not help the

  analyst to acquire data from web pages, but rather to store and organize the data.

• Question 52:

  A data analyst is attempting to understand how ice cream consumption is affected by different attributes. such as cost, temperature. and income level. Which of the following regression analyses should the data analyst perform to understand this relationship?

  A. Logistic

  B. Ordinary least squares

  C. Cox

  D. Polynomial

  Correct Answer: B

  Answer: B. Ordinary least squares Ordinary least squares (OLS) is a type of linear regression that is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when: The relationship between the predictor variable(s) and the response variable is reasonably linear. The response variable is a continuous numeric variable1. In this case, the data analyst is interested in understanding how ice cream consumption (the response variable) is affected by different attributes, such as cost, temperature, and income level (the predictor variables). Assuming that these variables have a linear relationship, OLS can be used to estimate the coefficients of the regression equation that best fits the data. OLS can also provide measures of goodness-of-fit, such as R-squared and adjusted R-squared, and test the significance of the coefficients using t-tests and F- tests2. Option A is incorrect, as logistic regression is used to fit a regression model that describes the relationship between one or more predictor variables and a binary response variable. Use when: The response variable is binary ?it can only take on two values1. Ice cream consumption is not a binary variable, but rather a continuous numeric variable. Option C is incorrect, as Cox regression is used to fit a regression model that describes the relationship between one or more predictor variables and a survival time response variable. Use when: The response variable is the time until an event of interest occurs, such as death, failure, or recovery3. Ice cream consumption is not a survival time variable, but rather a continuous numeric variable. Option D is incorrect, as polynomial regression is used to fit a regression model that describes the relationship between one or more predictor variables and a numeric response variable. Use when: The relationship between the predictor variable(s) and the response variable is non-linear1. If there is no evidence of non-linearity in the data, polynomial regression may not be appropriate, as it may overfit the data and produce unreliable estimates.

• Question 53:

  A data scientist wants to see which products make the most money and which products attract the most customer purchasing interest in their company.

  Which of the following data manipulation techniques would he use to obtain this information?

  A. Data append

  B. Data blending

  C. Normalize data

  D. Data merge

  Correct Answer: B

  The correct answer is B: Data blending. Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization and can then be processed or analyzed. Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question. Data append is incorrect. Data append is a process that involves adding new data elements to an existing database. An example of a common data append would be the enhancement of a company's customer files. A data append takes the information they have, matches it against a larger database of business data, allowing the desired missing data fields to be added. Normalize data is incorrect. Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set.

• Question 54:

  SIMULATION

  The director of operations at a power company needs data to help identify where company resources should be allocated in order to monitor activity for outages and restoration of power in the entire state. Specifically, the director wants to see the following:

  *

  County outages

  *

  Status

  *

  Overall trend of outages

  INSTRUCTIONS:

  Please, select each visualization to fit the appropriate space on the dashboard and choose an appropriate color scheme. Once you have selected all visualizations, please, select the appropriate titles and labels, if applicable. Titles and labels

  may be used more than once.

  If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

  

  A. Check the answer in explanation.

  Correct Answer: A

  Answer: Power outages This is a simulation question that requires you to create a dashboard with visualizations that meet the director's needs. Here are the steps to complete the task: Drag and drop the visualization that shows the county outages on the top left space of the dashboard. This visualization is a map of the state with different colors indicating the number of outages in each county. You can choose any color scheme that suits your preference, but make sure that the colors are consistent and clear. For example, you can use a gradient of red to show the counties with more outages and green to show the counties with less outages. Drag and drop the visualization that shows the status of the outages on the top right space of the dashboard. This visualization is a pie chart that shows the percentage of outages that are active, restored, or pending. You can choose any color scheme that suits your preference, but make sure that the colors are distinct and easy to identify. For example, you can use red for active, green for restored, and yellow for pending. Drag and drop the visualization that shows the overall trend of outages on the bottom space of the dashboard. This visualization is a line graph that shows the number of outages over time. You can choose any color scheme that suits your preference, but make sure that the color is visible and contrasted with the background. For example, you can use blue for the line and white for the background. Select appropriate titles and labels for each visualization. Titles and labels may be used more than once. For example, you can use "County Outages" as the title for the map, "Status" as the title for the pie chart, and "Trend" as the title for the line graph. You can also use "County", "Number of Outages", "Active", "Restored", "Pending", "Time", and "Number of Outages" as labels for the axes and legends of the visualizations.

• Question 55:

  A data analyst is creating a dashboard and trying to identify the type of information that should be included. Which of the following should the analyst consider first?

  A. Data refresh rate

  B. Consumer types

  C. Access permissions

  D. Data sources and attributes

  Correct Answer: D

  Explanation: The answer is D. Data sources and attributes. Short explanation: The data analyst should consider the data sources and attributes first when creating a dashboard, because they determine what kind of information can be included and how it can be displayed. The data sources and attributes define the origin, quality, format, and structure of the data that will be used for the dashboard. They also affect the data refresh rate, the consumer types, and the access permissions of the dashboard12

  A. Data refresh rate is not the first thing to consider, because it depends on the data sources and attributes. The data refresh rate is how often the data in the dashboard is updated or refreshed to reflect the latest changes. The data refresh rate can vary depending on the type, frequency, and availability of the data sources1 B. Consumer types are not the first thing to consider, because they depend on the data sources and attributes. The consumer types are the intended audiences or users of the dashboard, who may have different needs, preferences, and expectations for the dashboard. The consumer types can influence the design, layout, and functionality of the dashboard. However, the consumer types cannot be determined without knowing what kind of data is available and relevant for them1

  C. Access permissions are not the first thing to consider, because they depend on the data sources and attributes. The access permissions are the rules or policies that govern who can view, edit, or share the dashboard. The access permissions can protect the confidentiality, integrity, and availability of the data in the dashboard. However, the access permissions cannot be set without knowing what kind of data is involved and who needs to access it1

• Question 56:

  Encryption is a mechanism for protecting data.

  When should encryption be applied to data?

  Choose the best answer.

  A. When data is at rest.

  B. When data is at rest or in transit.

  C. When data is in transit.

  D. When data is at rest, unless you are using local storage.

  Correct Answer: B

  Correct answer B. When data is at rest or in transit. To provide maximum protection, encrypt data both in transit and at rest.

• Question 57:

  Given the diagram below:

  

  Which of the following types of sampling is depicted in the image?

  A. Stratified

  B. Random

  C. Cluster

  D. Systematic

  Correct Answer: D

  Explanation: Systematic sampling is a type of sampling where the sample is selected by following a fixed interval. For example, every 10th person in a list is chosen for the sample. In the image, the sample is selected by choosing every 3rd person in the line, starting from person number 1. This is an example of systematic sampling. References: Types of Sampling Techniques in Data Analytics You Should Know, Sampling Methods | Types, Techniques and Examples - Scribbr

• Question 58:

  A data analyst has been asked to merge the tables below, first performing an INNER JOIN and then a LEFT JOIN:

  

  Customer Table In-store Transactions ?

  

  Which of the following describes the number of rows of data that can be expected after performing both joins in the order stated, considering the customer table as the main table?

  A. INNER: 6 rows; LEFT: 9 rows

  B. INNER: 9 rows; LEFT: 6 rows

  C. INNER: 9 rows; LEFT: 15 rows

  D. INNER: 15 rows; LEFT: 9 rows

  Correct Answer: C

  An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is

  the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.

  To perform an INNER JOIN, we can use the following SQL query:

  SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

  This query will return 9 rows of data, as shown below:

  customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2

  | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08

  7|MILA|MORRIS|F|N|9|8000|2020-01-09 To perform a LEFT JOIN, we can use the following SQL query:

  SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

  This query will return 15 rows of data, as shown below:

  customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|202001-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09

  8||JENNY||DWARTH||F||Y||NULL||NULL||NULL

  As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.

  Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

  Reference: SQL Joins - W3Schools

• Question 59:

  Which of the following best describes a difference between JSON and XML?

  A. JSON is quicker to read and write.

  B. JSON has to use an end tag.

  C. JSON strings are longer

  D. JSON is much more difficult to parse.

  Correct Answer: A

  The best answer is A. JSON is quicker to read and write. JSON (JavaScript Object Notation) is a lightweight data-interchange format that is based on the JavaScript programming language and easy to understand and generate. JSON uses a simple syntax that consists of name-value pairs and arrays, and does not require any end tags or attributes. JSON is quicker to read and write than XML (Extensible Markup Language), which is a markup language that uses a tag structure to represent data items. XML has a more complex and verbose syntax that requires end tags, attributes, and namespaces123

• Question 60:

  You are working with a professional statistician to perform an analysis and would like to use a statistics package.

  Which one of the following would be the most appropriate?

  A. Rapid Miner.

  B. QLIK.

  C. Power BI.

  D. Minitab.

  Correct Answer: D

  Minitab is statistical analysis software. It can be used for learning about statistics as well as statistical research. Statistical analysis computer applications have the advantage of being accurate, reliable, and generally faster than computing statistics and drawing graphs by hand.