CompTIA DA0-001絶対合格: CompTIA Data+ Certification Exam - Topexam 最高の商品をお届けします我々の提供するCompTIAのDA0-001試験の資料のどのバーションでも各自のメリットを持っています。PDF版はパソコンでもスマホでも利用でき、どこでも読めます。ネットがあれば、オンライン版はどの電子商品でも使用できます。ソフト版は真実のCompTIAのDA0-001試験の環境を模倣して、あなたにCompTIAのDA0-001試験の本当の感覚を感じさせることができ、いくつかのパソコンでも利用できます。 CompTIA Data+ Certification Exam 認定 DA0-001 試験問題 (Q339-Q344):質問 # 339
An analyst modified a data set that had a number of issues. Given the original and modified versions:
Which of the following data manipulation techniques did the analyst use?
A. Deriving
B. Parsing
C. Imputation
D. Recoding
正解：D
解説：
The correct answer is B. Recoding.
Recoding is a data manipulation technique that involves changing the values or categories of a variable to make it more suitable for analysis.Recoding can be used to simplify or group the data, to correct errors or inconsistencies, or to create new variables from existing ones12 In the example, the analyst used recoding to change the values of Var001, Var002, Var003, and Var004 from numerical to textual form. The analyst also used recoding to assign meaningful labels to the values, such as
"Absent" for 0, "Present" for 1, "Low" for 2, "Medium" for 3, and "High" for 4. This makes the data more understandable and easier to analyze.
質問 # 340
Given the below:
Which of the following numbers represents a Type I error?
A. 0
B. 1
C. 2
D. 3
正解：A
質問 # 341
An analyst must obtain the average daily sales for the following week:
Which of the following must the analyst perform to obtain this value?
A. Data blending
B. Data normalization
C. Data aggregation
D. Data append
正解：C
解説：
Data aggregation is the process of compiling data from multiple sources and summarizing it into a single dataset. Data aggregation can be used to calculate statistics, such as averages, sums, counts, or percentages. In this case, the analyst must obtain the average daily sales for the following week, which is a statistic that can be calculated by aggregating the sales data from each day and dividing by the number of days. Data aggregation can be done using various tools and methods, such as spreadsheets, databases, or programming languages.
質問 # 342
Given the following table:
Which of the following describes the data quality issues with theagedata?
A. Consistency
B. Accuracy
C. Manipulation
D. Completeness
正解：A
解説：
Comprehensive and Detailed In-Depth
Data consistency refers to ensuring that all values in a dataset follow a uniform format and structure. In this dataset:
One age value is recorded as "65F," which includes anextra character (F)that makes it inconsistent with other numeric age values.
The date formats are inconsistent (6/1/22 vs. 6/19/2022), but this is unrelated to the age issue.
Option A (Completeness):Incorrect. There are no missing values in the age column.
Option B (Consistency):Correct.The age data should be consistently formatted asnumeric values only, without extra characters like "F." Option C (Accuracy):Incorrect. There is no evidence that theactual age valuesare incorrect, only that they are formatted inconsistently.
Option D (Manipulation):Incorrect. There is no indication that the data was intentionally altered for deception.
質問 # 343
An analyst is preparing a report that contains weather dat
a. 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. Rescale the data.
B. Normalize the data.
C. Aggregate the data.
D. Standardize the data.
正解：A
解説：
The analyst should rescale the data to fix this issue. Rescaling is a 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°F, the rescaled value in Celsius is:
Celsius = (68 - 32) * 5/9 Celsius = 20°C
Rescaling the data can help the analyst to report the temperatures in a consistent and accurate way, and to avoid any confusion or errors that may arise from using different scales. Rescaling can also make the data more comparable and compatible with other data sources or standards that use the same scale12.
