正真正銘のDA0-001日本語問題集で無料PDF問題で合格させる [Q106-Q129]

Explanation
Delimiting is the process of splitting a column of data into multiple columns based on a separator or delimiter character. Delimiting can help separate data that is combined or concatenated in one column into distinct values or categories. For example, if a column contains text values that are separated by commas, such as
“Comedy, Suspense”, delimiting can split this column into two columns, one for “Comedy” and one for
“Suspense”. Delimiting is different from other options, such as appending, merging, or concatenating, which are methods of combining or joining data from multiple columns or sources. In this case, the data analyst needs to determine the most popular movie genre based on the Genre column in the table. However, this column contains multiple genres for each movie, separated by commas. Therefore, the data analyst must delimit this column before this task can be completed. Therefore, the correct answer is D. References: Split text into different columns with functions – Office Support, How to Split Text in Excel (Using Formulas & Split Function)

Explanation
The data is presented in a CSV (comma-separated values) file format, which is a plain text format that stores tabular data. Each line of the file is a data record, and each record consists of one or more fields separated by commas. The first line of the file usually contains the names of the fields, also known as the header. In this case, the data has four fields: Name, Age, Gender, and Occupation. Therefore, the correct answer is B.
References: CSV File (What It Is & How to Open One), Comma-separated values – Wikipedia

Explanation
Data profiling is the process of examining data for statistics and information about the data, such as the structure, format, quality, and content of the data. Data profiling can help to understand the characteristics, patterns, relationships, and anomalies of the data, as well as to identify and resolve any errors, inconsistencies, or missing values in the data. Data profiling can be done using various tools and methods, such as spreadsheets, databases, or programming languages12.

Correct answer D. DML.
The Data Manipulation Language (DML) is used to work with the data stored in a database. DML includes the SELECT, INSERT, UPDATE, and DELETE commands.
The Data Definition Language (DDL) contains the commands used to create and structure a relational database. It includes the CREATE, ALTER, and DROP commands.
DDL and DML are the only two sublanguages of SQL.

You create an index to optimize performance when searching the table using the Find function and to establish uniqueness of table columns.
Indexing makes columns faster to query by creating pointers to where data is stored within a database. Imagine you want to find a piece of information that is within a large database. To get this information out of the database the computer will look through every row until it finds it.

Since you are trying to determine whether sales increased above the current average of $8000, the population mean for the null hypothesis is $8000; whereas the mean for the alternative hypothesis is greater than $8000. Hypotheses are always stated in terms of population you are working with, ruling out options A and B.
The test is whether sales are higher than the average of $8000 per day, which rules out option C.

Explanation
This is because a bar chart is a type of chart that shows the value or the amount of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A bar chart can be used to display and analyze the comparison, ranking, or proportion among the categories or groups, as well as identify any differences, similarities, or outliers in the data. For example, a bar chart can show which site has the highest or lowest percentage of new customers, as well as show how much each site contributes to the total percentage of new customers. The other types of charts are not the best charts to display the data. Here is why:
A line chart is a type of chart that shows the change or the trend of a single variable over time, such as the percentage of new customers over months or years in this case. A line chart can be used to display and analyze the movement, cycle, or pattern of the variable, as well as identify any peaks, valleys, or fluctuations in the data. For example, a line chart can show how the percentage of new customers increases or decreases over time, as well as show if there are any seasonal or periodic variations in the data.
A pie chart is a type of chart that shows the proportion or the percentage of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A pie chart can be used to display and analyze the composition, distribution, or share of the variable, as well as identify any segments, slices, or fractions in the data. For example, a pie chart can show how much each site represents of the total percentage of new customers, as well as show if there are any dominant or minor sites in the data.
A scatter chart is a type of chart that shows the relationship between two variables for each observation or unit in a data set, such as the percentage of new customers and another variable for each site in this case. A scatter chart can be used to display and analyze the correlation, trend, or pattern among the variables, as well as identify any outliers or clusters in the data. For example, a scatter chart can show if there is a positive, negative, or no correlation between the percentage of new customers and another variable, such as sales revenue or customer satisfaction.

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.

1. Duplicates
2. Misspellings
The most common data quality issues are difficult to resolve in Excel because of their rigidity. It forces analysts to do a ton of manual work, which results in a high probability of an error being introduced to the data set. Those common issues include:
– Blanks
– Nulls
– Outliers
– Duplicates
– Extra spaces
– Misspellings
– Abbreviations and domain-specific variations
– Formula error codes
When introduced, these errors can skew or even invalidate the resulting analysis. A smart tool would minimize the possibility of error by automating the manual work. In Excel, you might look for data quality issues in one of two ways. First, you might use auto filters on specific columns to scan for anomalies and blanks or you might use a pivot table to find gaps and discrepancies.
In either case, you’re scanning for the anomalies yourself. Suffice it to say that’s not a very efficient process. It also means accuracy is only as good as the analyst’s eye, so the probability of error varies throughout the day.

Explanation
The conclusion that is accurate at a 95% confidence interval is that in general, users who visit the new website are more likely to make a purchase. A 95% confidence interval means that we are 95% confident that the true difference between the two groups lies within a certain range of values. To calculate the 95% confidence interval, we can use the following formula:
CI = (p1 – p2) ± 1.96 * sqrt(p * (1 – p) * (1/n1 + 1/n2))
where p1 and p2 are the conversion rates for the test and control groups, respectively, p is the pooled conversion rate, n1 and n2 are the sample sizes for the test and control groups, respectively, and 1.96 is the z-score for a 95% confidence level.
Using this formula, we can calculate the 95% confidence interval for each country as follows:
Country | p1 | p2 | n1 | n2 | p | CI United States | 0.12 | 0.11 | 2000 | 2000 | 0.115 | (-0.006, 0.026) Germany |
0.06 | 0.04 | 1000 | 1000 | 0.05 | (-0.002, 0.042) United Kingdom | 0.09 | 0.07 | 1500 | 1500 | 0.08 | (-0.003,
0.053) France | 0.08 | 0.08 | 1200 | 1200 | 0.08 | (-0.024, 0.024) Canada | 0.05 | 0.03 | 800 | 800 | 0.04 | (-0.005,
0.045)
We can see that for all countries except France, the confidence interval does not include zero, which means that the difference between the test and control groups is statistically significant at a 95% confidence level.
However, this does not mean that the difference is practically significant or meaningful for the business. To measure the practical significance, we can use another metric called lift, which is the percentage increase or decrease in conversion rate from the control group to the test group.
Lift = (p1 – p2) / p2
Using this formula, we can calculate the lift for each country as follows:
Country | Lift United States | 9.09% Germany | 50% United Kingdom |28.57% France|0% Canada|66.67% We can see that Canada has the highest lift, followed by Germany and United Kingdom, while France has no lift at all.
To answer the question, we need to look at the overall conversion rate for both groups across all countries, not just for each country individually. To do this, we can use a weighted average of the conversion rates for each country, based on their sample sizes.
Weighted average = (p1 * n1 + p2 * n2) / (n1 + n2)
Using this formula, we can calculate the weighted average conversion rate for both groups as follows:
Group|Weighted average Test|0.084 Control|0.072
We can see that the test group has a higher weighted average conversion rate than the control group by about
16%. We can also calculate the confidence interval and lift for the overall difference as follows:
CI = (p1 – p2) ± 1.96 * sqrt(p * (1 – p) * (1/n1 + 1/n2)) = (0.084 – 0.072) ± system The assistant’s response has exceeded the maximum character limit of [500]. Please shorten your response or split it into multiple messages.

Explanation
This is because an INNER JOIN is a type of join that combines two tables based on a matching condition and returns only the rows that satisfy the condition. An INNER JOIN can be used to merge data from different tables that have a common column or a key, such as customer ID or order ID. To perform an INNER JOIN of the customer and order tables, we can use the following SQL statement:

This statement will select all the columns (*) from both tables and join them on the customer ID column, which is the common column between them. The result of this statement will be a new table that has seven rows and eight columns, as shown below:

The reason why there are seven rows and eight columns in the result table is because:
There are seven rows because there are six customers and six orders in the original tables, but only five customers have matching orders based on the customer ID column. Therefore, only five rows will have data from both tables, while one row will have data only from the customer table (customer 5), and one row will have no data at all (null values).
There are eight columns because there are four columns in each of the original tables, and all of them are selected and joined in the result table. Therefore, the result table will have four columns from the customer table (customer ID, first name, last name, and email) and four columns from the order table (order ID, order date, product, and quantity).

Explanation
Data encryption and data masking are two actions that can be taken when transmitting data to mitigate the chance of a data leak occurring. Data encryption means transforming data into an unreadable format that can only be decrypted with a key. Data masking means hiding or replacing sensitive data with fictitious or anonymized data. Both methods protect the confidentiality and integrity of the data in transit. References:
CompTIA Data+ Certification Exam Objectives, page 13

Explanation
The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep – CompTIA

According to the empirical rule, 68% of values are within one standard deviation, 95% are within two standard deviations, and 99.7% are within three standard deviations.

Explanation
The option that is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language is Python. Python is a popular and versatile programming language that can be used for various purposes, such as web development, software development, automation, machine learning, and data analysis. Python has many features and libraries that make it suitable for data analytics, such as its simple syntax, dynamic typing, multiple paradigms, built-in data structures, NumPy, pandas, matplotlib, scikit-learn, etc. The other options are not programming languages, but software applications or platforms that are used for data analytics or related tasks. SAS is a software suite that provides advanced analytics, business intelligence, data management, and predictive analytics capabilities. Microsoft Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities. IBM SPSS is a software package that offers statistical analysis, data mining, text analytics, and predictive analytics capabilities. Reference: Python For Data Analysis – DataCamp

Explanation
Two reasons to conduct data cleansing are:
To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable.
This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12 To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the data. This can help to identify and measure the patterns, changes, or relationships in the data over time13