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CompTIA DA0-001日本語のPDF問題格別な練習CompTIA Data+ Certification Exam (DA0-001日本語版)
質問 # 69
次のうち、一般的なデータ統合ツールではないものはどれですか?
- A. API
- B. XSS
- C. ELT
- D. ETL
正解：B
解説：
Cross-site Scripting (XSS) is a security vulnerability usually found in websites and/or web applications that accept user input.
XSS is a client-side vulnerability that targets other application users, while SQL injection is a server-side vulnerability that targets the application's database. How do I prevent XSS in PHP? Filter your inputs with a whitelist of allowed characters and use type hints or type casting.
質問 # 70
フラット テキスト ファイルと他のデータ タイプの違いは次のうちどれですか?
- A. データはデリミタで区切られます。
- B. 定義された行にデータが格納されます。
- C. データはキーと値のペアで定義されます。
- D. データはマークアップ言語で格納されます。
正解：A
解説：
A flat text file is a type of data file that contains only plain text without any formatting or markup. Data in a flat text file is usually separated by a delimiter, which is a character that marks the boundary between different fields or values. For example, a comma-separated values (CSV) file is a flat text file that uses commas as delimiters. Other common delimiters are tabs, spaces, semicolons, and pipes. Therefore, the correct answer is A. Reference: Plain text - Wikipedia, Comparison of document markup languages - Wikipedia
質問 # 71
次のうち、離散データ型の例はどれですか?
- A. 8in (20cm)
- B. 2.5mi (4km)
- C. 5 人の子供
- D. 10.7lbs (4.9kg)
正解：C
解説：
Explanation
A discrete data type is a data type that can only take on a finite number of values, such as integers or categories. An example of a discrete data type is the number of kids, as it can only be a whole number. The other options are examples of continuous data types, as they can take on any value within a range. The length in inches or centimeters, the distance in miles or kilometers, and the weight in pounds or kilograms are all continuous data types. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy
質問 # 72
データセットはマルチメディア技術を使用して記録されました。解釈する上で必要なステップは次のうちどれですか?
- A. 転写
- B. 逐次解析
- C. 構造方程式モデリング
- D. サンプリング
正解：A
解説：
Explanation
The correct answer is B. Transcription.
Transcription is a necessary step on the way to interpretation when a data set was recorded using multimedia technology. Multimedia technology refers to the use of various forms of media, such as audio, video, images, and text, to capture and present information1 Transcription is the process of converting multimedia data into written or textual form, which can then be analyzed using various methods and tools2 Transcription can help to make the data more accessible, searchable, and manageable, as well as to preserve the data for future use.
Structural equation modeling is not correct, because it is a statistical technique that tests the causal relationships between multiple variables using observed and latent variables. Structural equation modeling is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sequential analysis is not correct, because it is a method of analyzing the order and timing of events or behaviors in a data set. Sequential analysis is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sampling is not correct, because it is the process of selecting a subset of data from a larger population for analysis. Sampling is not a necessary step on the way to interpretation, but rather a preliminary step that can be done before collecting or analyzing the data.
質問 # 73
電力会社の業務部長は、州全体での電力の停止と復旧の活動を監視するために、会社のリソースをどこに割り当てる必要があるかを特定するのに役立つデータを必要としています。具体的には、監督は次のことを確認したいと考えています。
*郡の停止
* 状態
※停電の全体傾向
手順：
ダッシュボードの適切なスペースに収まるように各ビジュアライゼーションを選択し、適切な配色を選択してください。すべてのビジュアライゼーションを選択したら、該当する場合は適切なタイトルとラベルを選択してください。タイトルとラベルは複数回使用できます。
いつでもシミュレーションを初期状態に戻したい場合は、[すべてリセット] ボタンをクリックしてください。
- A. 停電
- B. 電源
正解：A
質問 # 74
外部キー関係を使用して結合したい 2 つのデータベース テーブルがあります。
このアクションを最もよく表す用語はどれですか?
- A. ブレンディング。
- B. 追記。
- C. ミキシング。
- D. マージ。
正解：D
解説：
Explanation
Data merging is the process of combining two or more data sets into a single data set. Most often, this process is necessary when you have raw data stored in multiple files, worksheets, or data tables, that you want to analyze all in one go.
質問 # 75
リレーショナル データ ストリーム管理システムに保持されるデータを管理するために設計された、プログラミングで使用されるドメイン固有言語は次のうちどれですか?
- A. SAS
- B. SQL
- C. Python
- D. R
正解：B
質問 # 76
エンティティ関係図でフィールドが必須であることを示すテスト形式オプションはどれですか?
- A. 太字。
- B. 大文字。
- C. イタリック体。
- D. 下線。
正解：A
質問 # 77
次のデータ テーブルがあるとします。
次の MDM プロセスのうち、最初に実行する必要があるのはどれですか?
- A. データフィールド名の標準化
- B. データ辞書の作成
- C. 規制の遵守
- D. 複数のデータ フィールドの統合
正解：B
解説：
Explanation
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 the analyst 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 # 78
データ アナリストは、2020 年第 2 四半期の取締役会向けの売上レポートを作成するよう求められます。このレポートには、第 2 四半期までの業績のレビューが含まれます。取締役会は、数字が確定した後、2020 年 7 月 15 日に開催されます。データ アナリストが作成する必要があるレポートの種類は次のうちどれですか?
- A. ダイナミック
- B. 静的
- C. リアルタイム
- D. セルフサービス
正解：B
解説：
A dynamic report is a type of report that shows data that changes or updates automatically based on certain criteria or parameters. A dynamic report can allow users to interact with the data, filter it, drill down into it, or visualize it in different ways. A dynamic report is suitable for situations where the data changes frequently or where real-time or near-real-time data is needed for decision making or analysis. In this case, the data analyst is asked to create a sales report for the second-quarter 2020 board meeting, which will include a review of the business's performance through the second quarter. The board meeting will be held on July 15, 2020, after the numbers are finalized. This means that the data analyst does not need to show real-time or dynamic data, but rather a fixed and accurate view of the sales data for the second quarter. Therefore, a static report would be the best way to meet this stakeholder requirement. Therefore, the correct answer is A. Reference: [What are Dynamic Reports? | Sisense], Static vs Dynamic Reports - What's The Difference? | datapine
質問 # 79
アナリストは、従業員イントラネット サイトの分析ダッシュボードを作成して、検索機能を改善し、関連情報を表示し、最新の FAQ ページを維持する必要があります。次のビジュアライゼーションのうち、従業員が探しているものを最もよく表すものはどれですか?
- A. 散布図
- B. ワード クラウド
- C. 円グラフ
- D. ヒストグラム
正解：B
質問 # 80
アナリストは四半期レポートを分析するときに、売上高比率の変化に気づきます。アナリストは次のうちどれを実施していますか?
- A. 統計分析
- B. リンク分析
- C. ギャップ分析
- D. 傾向分析
正解：D
質問 # 81
アナリストは迅速な分析を行う必要があります。アナリストがデータに対して実行する最初のステップは次のうちどれですか?
- A. 初期分析を行い、パレート図を使用します。
- B. リンク分析を行い、接続点を図解します。
- C. 傾向分析を行い、散布図を使用します。
- D. 探索的分析を行い、記述統計を使用します。
正解：D
解説：
Explanation
The first step the analyst should perform with the data is to conduct an exploratory analysis and use descriptive statistics. Exploratory analysis is a type of analysis that aims to summarize the main characteristics of the data, identify patterns, outliers, and relationships, and generate hypotheses for further investigation. Descriptive statistics are numerical measures that describe the central tendency, variability, and distribution of the data, such as mean, median, mode, standard deviation, range, quartiles, etc. Exploratory analysis and descriptive statistics can help the analyst gain a better understanding of the data and its quality, as well as prepare the data for further analysis.
質問 # 82
次のような食料品店の注文があるとします。
次のロジックを使用してテーブルに対してクエリが実行された場合:
Order_Total > 132 OR (Order Total >= 25 AND Order_Total < 74)
クエリによって返される注文の数は次のうちどれですか?
- A. 0
- B. 1
- C. 2
- D. 3
正解：D
解説：
Based on the query logic provided: Order_Total > 132 OR (Order Total >= 25 AND Order_Total < 74), we can manually determine which order totals fit this criteria. By examining the image, these are the Order_Total values that match:
* 132.49 (greater than 132)
* 108.99 (greater than or equal to 25 and less than 74)
* 96.19 (greater than or equal to 25 and less than 74)
* 74.49 (greater than or equal to 25 and less than 74)
* 41.99 (greater than or equal to 25 and less than 74)
* 31.29 (greater than or equal to 25 and less than 74)
Thus, six orders satisfy the given conditions.
質問 # 83
会社のマーケティング部門は、来月プロモーション キャンペーンを実施したいと考えています。チームのデータ アナリストは、顧客がどのくらい最近に製品を購入したか、頻度、金額を調べて顧客セグメンテーションを実行するように依頼されました。この実践では次のタイプの分析のうちどれが考慮されますか?
- A. ギャップ
- B. 規範的
- C. トレンド
- D. カスター
正解：D
解説：
Explanation
Customer segmentation is a type of cluster analysis, which is a method of grouping data points based on their similarities or differences. Cluster analysis can help identify patterns and trends in the data, as well as target specific groups of customers for marketing purposes. One common technique for customer segmentation is RFM analysis, which stands for recency, frequency, and monetary value. This technique assigns a score to each customer based on how recently they bought the product, how often they buy the product, and how much they spend on the product. These scores can then be used to create clusters of customers with different characteristics and preferences. Therefore, the correct answer is D. References: Cluster Analysis - Statistics Solutions, RFM Analysis: The Ultimate Guide for Customer Segmentation
質問 # 84
次のことを前提とします。
トレンド分析のためにテーブルを変換するときにアナリストが行うべき最も重要なことはどれですか?
- A. 日付が同じ形式になるように修正します。
- B. 不足しているコストが null の場合、そのコストを入力します。
- C. テーブルを2つのテーブルに分割し、主キーを作成します。
- D. 拡張コスト フィールドを計算フィールドに置き換えます。
正解：A
解説：
Correcting the dates so they have the same format is the most important thing for an analyst to do when transforming the table for a trend analysis. Trend analysis is a method of analyzing data over time to identify patterns, changes, or relationships. To perform a trend analysis, the data needs to have a consistent and comparable format, especially for the date or time variables.
In the example, the date purchased column has two different formats: YYYY-MM-DD and MM/DD/YYYY.
This could cause errors or confusion when sorting, filtering, or plotting the data over time. Therefore, the analyst should correct the dates so they have the same format, such as YYYY-MM-DD, which is a standard and unambiguous format.
質問 # 85
データ アナリストは、アイスクリームの消費がさまざまな属性によってどのような影響を受けるかを理解しようとしています。コストや温度など。そして収入水準。この関係を理解するためにデータ アナリストが実行すべき回帰分析は次のうちどれですか?
- A. 多項式
- B. 物流
- C. 通常の最小二乗法
- D. コックス
正解：C
解説：
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 dat a. 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.
質問 # 86
データ アナリストは、売上の 12 か月の移動平均を計算する売上レポートを作成するように依頼されました。レポートが 2020 年 11 月 1 日に発行される場合、次の月のうち、レポートの表紙を叫ぶのはどれですか?
- A. 2019 年 11 月 1 日から 2020 年 10 月 31 日まで
- B. 2019 年 10 月 31 日から 2020 年 10 月 31 日まで
- C. 2019年10月1日～2020年10月31日
- D. 2020年10月31日～2021年11月1日
正解：C
解説：
Explanation
The report should cover the months from October 1, 2019 to October 31, 2020. A rolling 12-month average is a type of moving average that calculates the average of the last 12 months of data for each month. It is useful for smoothing out seasonal fluctuations and identifying long-term trends in the data. To calculate the rolling
12-month average for sales for November 1, 2020, the analyst needs to use the sales data from the previous 12 months, starting from November 1, 2019 and ending on October 31, 2020. The other options are either too short or too long to cover the required period.
質問 # 87
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