DA0-001 LATEST TEST BOOTCAMP & LATEST DA0-001 TEST CAMP

DA0-001 Latest Test Bootcamp & Latest DA0-001 Test Camp

DA0-001 Latest Test Bootcamp & Latest DA0-001 Test Camp

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CompTIA Data+ Certification Exam Sample Questions (Q340-Q345):

NEW QUESTION # 340
While reviewing survey data, a research analyst notices data is missing from all the responses to a single question. Which of the following methods would BEST address this issue?

  • A. Replace missing data.
  • B. Replace redundant data.
  • C. Remove duplicate data.
  • D. Remove invalid data.

Answer: A

Explanation:
This is because missing data is a type of data quality issue that occurs when data is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis or process. Missing data can be caused by various factors, such as human error, system error, or non-response. Missing data can be addressed by using various methods, such as replacing missing data, which means filling in or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression. The other methods are not used to address missing data. Here is why:
Remove duplicate data is a type of method that eliminates or reduces duplicate data, which is a type of data quality issue that occurs when data is repeated or copied in a data set. Removing duplicate data does not address missing data, but rather affects the quantity and validity of the data.
Replace redundant data is a type of method that eliminates or reduces redundant data, which is a type of data quality issue that occurs when data is unnecessary or irrelevant for the analysis or purpose. Replacing redundant data does not address missing data, but rather affects the efficiency and performance of the analysis or process.
Remove invalid data is a type of method that eliminates or reduces invalid data, which is a type of data quality issue that occurs when data is incorrect or inaccurate in a data set. Removing invalid data does not address missing data, but rather affects the validity and reliability of the analysis or process.


NEW QUESTION # 341
An analyst is compiling a series of reports for the new executive board to review. Which of the following elements provides a snapshot of what is contained in the reports for the executives who do not have time to focus on the details?

  • A. Reference data sources
  • B. Instruction page
  • C. Observations and insights
  • D. Tables

Answer: C

Explanation:
Observations and insights provide a concise summary of the key findings in a report. Executives, who often lack the time to analyze detailed datasets, benefit from these summarized interpretations, which highlight the most relevant trends, anomalies, and conclusions derived from the data.


NEW QUESTION # 342
A data analyst has received a data set that contains actual and projected sales for the fourth quarter of 2019. Which of the following statistical methods should the analyst use to find the measure of dispersion?

  • A. Variance
  • B. Correlation
  • C. Confidence interval
  • D. Mean

Answer: A


NEW QUESTION # 343
Which one the following is not considered an aggregate function?

  • A. SUM
  • B. SELECT
  • C. MAX
  • D. MIN

Answer: B

Explanation:
The option that is not considered an aggregate function is SELECT. An aggregate function is a function that performs a calculation on a set of values and returns a single value. Examples of aggregate functions are SUM, MIN, MAX, AVG, COUNT, etc. SELECT is not an aggregate function, but a SQL command that is used to select data from a table or a query. Reference: SQL Aggregate Functions - W3Schools


NEW QUESTION # 344
Which of the following best describe qualitative data? (Select two).

  • A. Nominal
  • B. Real-time
  • C. Discrete
  • D. Batch
  • E. Continuous
  • F. Ordinal

Answer: A,F

Explanation:
Comprehensive and Detailed In-Depth
Qualitative data refers to non-numeric information that describes qualities or characteristics. It is often categorized based on attributes or properties and is typically descriptive in nature.
Ordinal Data: This type of qualitative data represents categories with a meaningful order but without a consistent interval between them. Examples include rankings (e.g., customer satisfaction levels such as 'satisfied,' 'neutral,' 'dissatisfied') where the order matters, but the difference between ranks is not uniform.
Nominal Data: This qualitative data type consists of categories without any inherent order. Examples include gender, race, or types of products. Each category is distinct, and there is no logical sequence.
In contrast, options A (Discrete) and D (Continuous) refer to quantitative data types:
Discrete Data: Quantitative data that can take on only specific, distinct values (e.g., the number of students in a class).
Continuous Data: Quantitative data that can take on any value within a range (e.g., height, weight).
Options C (Batch) and F (Real-time) pertain to data processing methods rather than data types.
Reference:
CompTIA Partners


NEW QUESTION # 345
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