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認定するCompTIA DA0-001 |真実的なDA0-001受験記試験 |試験の準備方法CompTIA Data+ Certification Exam実際試験

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Comptia Data+認証としても知られるComptia DA0-001は、データ管理の分野で働く個人の知識とスキルをテストするために設計されたベンダー中立認証試験です。この認定は、専門家が大規模なデータセットを収集、分析、解釈し、分析に基づいて情報に基づいた決定を下す能力を検証します。この試験では、データストレージ、データ品質、データの視覚化、データガバナンス、データセキュリティなど、幅広いトピックをカバーしています。これは、データ分析またはデータ管理のキャリアを追求することに関心のある個人にとって理想的な認定です。

CompTIA DA0-001、またはCompTIA Data+認定は、データ管理分野の個人のスキルと知識を測定するエントリーレベルの認定試験です。この試験は、データ管理のキャリアを追求し、データ分析、データベース設計、データ処理の知識を検証したい個人を対象としています。試験は、データストレージと管理、データセキュリティ、データ分析、およびデータベース設計を含むさまざまなトピックをカバーしています。

CompTIA Data+認定試験は、世界的に認められた業界標準の認定試験です。この認定試験は、データ管理におけるスキルと知識をITプロフェッショナルが証明するのに役立ちます。この認定試験は、金融、医療、小売などのデータ駆動型産業、ITサポートやオペレーションで働く人々に最適です。

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真実的CompTIA DA0-001｜高品質なDA0-001受験記試験｜試験の準備方法CompTIA Data+ Certification Exam実際試験

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CompTIA Data+ Certification Exam 認定 DA0-001 試験問題 (Q261-Q266):

質問 # 261
Given the following data table:

Which of the following are appropriate reasons to undertake data cleansing? (Select two).

A. Duplicate data
B. Normalized data
C. Invalid data
D. Non-parametric data
E. Missing data
F. Redundant data

正解：C、E

解説：
Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:
* Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.
* Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.
Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn' t assume a normal distribution. Duplicate data and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.
References:
* Understanding the importance of data quality and the impacts of missing and invalid data on research outcomes1.
* Best practices in data cleansing2.
Data cleansing is required for various reasons, two of which are missing data (B) and invalid data (D). From the table provided, we can infer the necessity of cleansing in the context of ensuring data integrity and consistency. Missing data refers to the absence of data where it is expected, which can hinder analysis due to incomplete information. Invalid data refers to data that is incorrect, out of range, or in an inappropriate format, which can lead to inaccuracies in any analysis or report. Both these issues can significantly affect the outcomes of any data-related operations and thus need to be rectified through the data cleansing process.

質問 # 262
An organization wants to evaluate whether project activities are within the set projections and in line to meet the desired project targets. Which of the following types of analysis is best suited for this situation?

A. Descriptive analysis
B. Trend analysis
C. Performance analysis
D. Exploratory analysis

正解：C

解説：
Comprehensive and Detailed In-Depth
Performance analysisis used toassess whether activities, projects, or processes are meeting predefined goals. It compares actual performance against benchmarks or expectations.
Option A (Trend analysis):Incorrect. Trend analysis looks at data over time to identify patterns or movements, but it does not measure progress against goals.
Option B (Performance analysis):Correct.This type of analysis is specifically used toevaluate progress against projections and targets.
Option C (Descriptive analysis):Incorrect. Descriptive analysis summarizes historical data but does not evaluate whether targets are being met.
Option D (Exploratory analysis):Incorrect. Exploratory analysis is used todiscover patterns and anomaliesrather than to track progress against predefined objectives.

質問 # 263
Consider the following dataset which contains information about houses that are for sale:

Which of the following string manipulation commands will combine the address and region name columns to create a full address?
full_address------------------------- 85 Turner St, Northern Metropolitan 25 Bloomburg St, Northern Metropolitan 5 Charles St, Northern Metropolitan 40 Federation La, Northern Metropolitan 55a Park St, Northern Metropolitan

A. SELECT CONCAT(regionname, '-' , address) AS full_address FROM melb LIMIT 5;
B. SELECT CONCAT(regionname, ' , ' , address) AS full_address FROM melb LIMIT 5
C. SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT 5;
D. SELECT CONCAT(address, '-' , regionname) AS full_address FROM melb LIMIT 5;

正解：C

解説：
Explanation
The correct answer is A: SELECT CONCAT(address, ' , ' , regionname) AS full_address FROM melb LIMIT
5; String manipulation (or string handling) is the process of changing, parsing, splicing, pasting, or analyzing strings. SQL is used for managing data in a relational database. The CONCAT () function adds two or more strings together. Syntax CONCAT(stringl, string2,... string_n) Parameter Values Parameter Description stringl, string2, string_n Required. The strings to add together.

質問 # 264
Given the following data tables:

Which of the following MDM processes needs to take place FIRST?

A. Standardization of data field names
B. Compliance with regulations
C. Creation of a data dictionary
D. Consolidation of multiple data fields

正解：C

解説：
This is because a data dictionary is a type of document that defines and describes the data elements, attributes, and relationships in a database or a data set. A data dictionary can be used to facilitate the MDM (Master Data Management) process, which is a process that aims to ensure the quality, consistency, and accuracy of the data across different sources and systems. By creating a data dictionary first, the analyst can establish a common understanding and standardization of the data field names, types, formats, and meanings, as well as identify any potential issues or conflicts in the data, such as missing values, duplicate values, or inconsistent values. The other MDM processes can take place after creating a data dictionary. Here is why:
Compliance with regulations is a type of MDM process that ensures that the data meets the legal and ethical requirements and standards of the industry or the organization. Compliance with regulations can take place after creating a data dictionary, because the data dictionary can help theanalyst to identify and apply the relevant rules and policies to the data, such as data privacy, security, or retention.
Standardization of data field names is a type of MDM process that ensures that the data field names are consistent and uniform across different sources and systems. Standardization of data field names can take place after creating a data dictionary, because the data dictionary can provide a reference and a guideline for naming and labeling the data fields, as well as resolving any discrepancies or ambiguities in the data field names.
Consolidation of multiple data fields is a type of MDM process that combines or merges the data fields from different sources or systems into a single source or system. Consolidation of multiple data fields can take place after creating a data dictionary because the data dictionary can help the analyst to map and match the data fields from different sources or systems based on their definitions and descriptions, as well as eliminating any redundant or duplicate data fields.

質問 # 265
Given the following tables:

Which of the following will be the dimensions from a FULL JOIN of the tables above?

A. Four rows and four columns
B. Three rows and four columns
C. Two rows and three columns
D. Four rows and two columns

正解：A

質問 # 266
......

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