[2024年06月25日] 究極のDA0-001日本語準備ガイド！無料最新のCompTIA練習テスト問題集
今すぐゲットせよ！高評価CompTIA DA0-001日本語試験問題集
質問 # 142
アナリストは IT ドキュメントを作成し、そのドキュメントで使用される技術用語を説明する必要があります。アナリストが技術用語の説明を含める必要があるのは次のうちどれですか?
- A. システム図
- B. ユーザー要件
- C. インデックス
- D. 用語集
正解：D
質問 # 143
次のデータ型のうち、4Ac1 を最もよく表すのはどれですか? (2 つ選択してください)。
- A. 浮動小数点数
- B. 文字列
- C. ブール値
- D. 数値
- E. 英数字
- F. 記号
正解：B、E
質問 # 144
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. 大文字。
- B. 下線。
- C. イタリック体。
- D. 太字。
正解：D
質問 # 145
病院データベースのテーブルには、患者の身長 (インチ) の列と患者の身長 (センチメートル) の列があります。これは次の例です。
- A. 無効なデータ
- B. 従属データ。
- C. 冗長データ
- D. 重複データ。
正解：C
解説：
Explanation
This is because redundant data is a type of data that is unnecessary or irrelevant for the analysis or purpose, which can affect the efficiency and performance of the analysis or process. Redundant data can be caused by having multiple data fields that store the same or similar information, such as patient height in inches and patient height in centimeters in this case. Redundant data can be eliminated or reduced by using data cleansing techniques, such as removing or merging the redundant data fields. The other types of data are not examples of data that is unnecessary or irrelevant for the analysis or purpose. Here is what they mean in terms of data quality:
Dependent data is a type of data that relies on or is influenced by another data field or value, such as a formula or a calculation that uses other data fields or values as inputs or outputs. Dependent data can be useful or important for the analysis or purpose, as it can provide additional information or insights based on the existing data.
Duplicate data is a type of data that is repeated or copied in a data set, which can affect the quality and validity of the analysis or process. Duplicate data can be caused by having multiple records or rows that have the same or similar values for one or more data fields or columns, such as customer ID or order ID.
Duplicate data can be eliminated or reduced by using data cleansing techniques, such as removing or filtering out the duplicate records or rows.
Invalid data is a type of data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis or process. Invalid data can be caused by having values that do not match the expected format, type, range, or rule for a data field or column, such as an email address that does not have an @ symbol or a date that does not follow the YYYY-MM-DD format. Invalid data can be eliminated or reduced by using data cleansing techniques, such as validating or correcting the invalid values.
質問 # 146
会社のマーケティング部門は、来月販促キャンペーンを行いたいと考えています。チームのデータ アナリストは、顧客が最近製品を購入した時期、頻度、価値を調べて、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。次のタイプの分析のうち、このプラクティスが考慮されるのはどれですか?
- A. トレンド
- B. カスター
- C. 規範的
- D. ギャップ
正解：B
質問 # 147
探索的分析を最もよく表すものは次のうちどれですか?
- A. 分布を決定するために算術代数の使用が含まれます。
- B. 特定の仮説のテストが含まれます
- C. パフォーマンス追跡のためのデータセットの調査の分析が含まれます。
- D. 観察を理解するために記述統計の使用が含まれます。
正解：D
解説：
Explanation
answer: A. Involves the use of descriptive statistics to understand observations.
Exploratory data analysis (EDA) is a method of analyzing and investigating data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA involves the use of descriptive statistics, such as mean, median, mode, standard deviation, frequency, or percentage, to understand the distribution, central tendency, variability, and relationship of the data. EDA helps to see what the data can reveal beyond the formal modeling or hypothesis testing, and provides a better understanding of data set variables and the interactions between them1.
質問 # 148
アナリストが、多くの問題を抱えたデータセットを変更しました。元のバージョンと変更されたバージョンを考慮すると、次のようになります。
アナリストが使用したデータ操作手法は次のうちどれですか?
- A. 代入
- B. 解析中
- C. 記録
- D. 導出
正解：C
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 # 149
次のうち、離散データ型の例はどれですか?
- A. 2.5mi (4km)
- B. 10.7lbs (4.9kg)
- C. 8in (20cm)
- D. 5 人の子供
正解：D
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 # 150
'テーブルの代わりにデータベース ビューを使用する最も良い理由は次のうちどれですか?
- A. ビューでは一時データを保存できます。一方、テーブルはそうではありません。
- B. ビューにより、反復的で複雑なデータ結合の必要性が軽減されます。
- C. ビューでは複数のデータ ソースを結合できますが、テーブルでは結合できません。
- D. ビューを使用して機密情報を制限できます。
正解：B
解説：
Explanation
Views are virtual tables that are created by querying one or more base tables or other views. Views do not store any data, but only show the result of a query. One of the main advantages of using views is that they can reduce the need for repetitive, complex data joins. For example, if a query involves joining multiple tables with many conditions, creating a view can simplify the query and make it easier to reuse. Therefore, the correct answer is A. References: [What is a Database View? | Definition & Examples - Vertabelo], [Database Views - GeeksforGeeks]
質問 # 151
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. データ辞書の作成
- B. データフィールド名の標準化
- C. 複数のデータ フィールドの統合
- D. 規制の遵守
正解：A
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 # 152
通常、標準のヒート マップにはいくつの変数が表示されますか?
- A. 0
- B. 1
- C. 2
- D. 3
正解：B
質問 # 153
次の顧客テーブルと注文テーブルがあるとします。
次のうち、テーブルの INNER JOIN を実行した後に存在するデータの行と列の数を説明しているのはどれですか?
- A. 7 行 8 列
- B. 8 行 7 列
- C. 9 行 5 列
- D. 5 行 8 列
正解：A
解説：
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).
質問 # 154
データ アナリストは、以下のデータ セットを使用して第 1 四半期の売上の平均を計算する必要があります。
平均は次のうちどれ？
- A. $2,667.60
- B. $3,082.72
- C. $2,466.18
- D. $12,330.88
正解：B
質問 # 155
アナリストはユーザー用に新しいダッシュボードを構築しています。ユーザーとの最初の会話の後。アナリストはダッシュボードのモックアップを作成しました。アナリストがモックアップを作成した理由を最もよく説明しているものは次のうちどれですか?
- A. ダッシュボードを本番環境にデプロイした後にクライアントに送信します。
- B. ダッシュボード開発を開始する前に重要な詳細を確認するため
- C. 寸法と寸法を特定するため
- D. 最終的なダッシュボード設計についてクライアントの承認を受けるため
正解：B
解説：
Explanation
answer: C. To confirm important details before dashboard development begins.
A dashboard mockup is a prototype of a finished dashboard directly in the product. It is a way to visualize the layout, design, and functionality of the dashboard before it is built with real data and code. A dashboard mockup can help the analyst to confirm important details with the user, such as the business objectives, the key performance indicators, the data sources, the filters, the charts, and the interactivity. By creating a dashboard mockup, the analyst can get immediate feedback and validation from the user, and avoid wasting time and resources on developing a dashboard that does not meet the user's expectations or needs1.
質問 # 156
アナリストは、新規顧客の割合が最も高いサイトを特定するダッシュボードを設計しています。アナリストは、ダッシュボードに含める適切なグラフを選択する必要があります。次のデータが利用可能です。
データを最適に表示するには、次のどのタイプのチャートを考慮する必要がありますか?
- A. サイトと新規顧客データの割合を使用した折れ線グラフを含めます。
- B. サイトと新規顧客データの割合を使用した棒グラフを含めます。
- C. サイトと新規顧客データの割合を使用した円グラフを含めます。
- D. サイトと新規顧客データの割合を使用した散布図を含めます。
正解：B
質問 # 157
次のうち、テキストの正しいデータ型はどれですか?
- A. 文字列
- B. ブール値
- C. 整数
- D. フロート
正解：A
解説：
The correct data type for text is string. A string is a data type that represents a sequence of characters, such as letters, numbers, symbols, or spaces. A string can be enclosed by single quotes (' ') or double quotes (" ") in most programming languages. For example, 'Hello', "World", and "123" are all strings. The other options are not data types for text, but for other kinds of values. A boolean is a data type that represents a logical value, either true or false. An integer is a data type that represents a whole number, such as 1, 0, or -5. A float is a data type that represents a number with a fractional part, such as 3.14, 0.5, or -2.7. Reference: Data Types - W3Schools
質問 # 158
データ クレンジングを実行する理由は次のうちどれですか? (2 つ選択してください)。
- A. データセットを確認するには
- B. KPl を追跡するため
- C. トレンドを計算する
- D. Webスクレイピングを実行する場合
- E. 精度を上げるため
- F. サンプルサイズを増やす場合
正解：C、E
解説：
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 dat a. This can help to identify and measure the patterns, changes, or relationships in the data over time13
質問 # 159
転送中のデータの例は何ですか?
- A. ネットワーク上のデータ。
- B. ハードディスク上のデータ。
- C. スマートフォンのデータ。
- D. パソコンのメモリーにあるデータ。
正解：A
解説：
A data network is a system designed to transfer data from one network access point to one other or more network access points via data switching, transmission lines, and system controls. Data networks consist of communication systems such as circuit switches, leased lines, and packet switching networks.
質問 # 160
調査アナリストは、分析対象のデータが他のデータポイントに接続されているかどうかを判断したいと考えています。次のうち、実施するのに最適なタイプの分析はどれですか?
- A. トレンド分析
- B. 探索的分析
- C. パフォーマンス分析
- D. リンク分析
正解：D
解説：
This is because link analysis is a type of analysis that determines whether the data being analyzed is connected to other datapoints, such as entities, events, or relationships. Link analysis can be used to identify and visualize the patterns, networks, or associations among the datapoints, as well as measure the strength, direction, or frequency of the connections. For example, link analysis can be used to determine if there is a connection between a customer's purchase history and their loyalty program status. The other types of analysis are not the best types of analysis to conduct to determine whether the data being analyzed is connected to other datapoints. Here is why:
Trend analysis is a type of analysis that determines whether the data being analyzed is changing over time, such as increasing, decreasing, or fluctuating. Trend analysis can be used to identify and visualize the patterns, cycles, or movements in the data points, as well as measure the rate, direction, or magnitude of the changes. For example, trend analysis can be used to determine if there is a change in a company's sales revenue over a period of time.
Performance analysis is a type of analysis that determines whether the data being analyzed is meeting certain goals or objectives, such as targets, benchmarks, or standards. Performance analysis can be used to identify and visualize the gaps, deviations, or variations in the data points, as well as measure the efficiency, effectiveness, or quality of the outcomes. For example, performance analysis can be used to determine if there is a gap between a student's test score and their expected score based on their previous performance.
Exploratory analysis is a type of analysis that determines whether there are any insights or discoveries in the data being analyzed, such as patterns, relationships, or anomalies. Exploratory analysis can be used to identify and visualize the characteristics, features, or behaviors of the data points, as well as measure their distribution, frequency, or correlation. For example, exploratory analysis can be used to determine if there are any outliers or unusual values in a dataset.
質問 # 161
データ アナリストは、以下のテーブルを使用して顧客の注文の完全外部結合を実行する必要があります。
注文数量の平均は次のうちどれですか?
- A. 73.5
- B. 81.5
- C. 76.5
- D. 78.8
正解：B
解説：
The correct answer is D. OUTER JOIN, seven rows.
An OUTER JOIN is a type of SQL join that returns all the rows from both tables, regardless of whether there is a match or not. If there is no match, the missing side will have null values. An OUTER JOIN can be either a LEFT JOIN, a RIGHT JOIN, or a FULL JOIN, depending on which table's rows are preserved1 Using the example tables, a FULL OUTER JOIN query would look like this:
SELECT Cust_id, Order_id, Order_qty FROM Sales_table FULL OUTER JOIN Order_table ON Sales_table.Order_id = Order_table.Order_id; The result of this query would be:
Cust_id | Order_id | Order_qty --------±---------±--------- 1 | 1 | 100 2 | 2 | 50 3 | 3 | 25 4 | 4 | 75 NULL | 5 | 10 NULL | 6 | 20 NULL | 7 | 15 As you can see, the query returns seven rows, one for each order in either table. The orders that are not in the Sales_table have null values for the Cust_id column.
To find the mean of the order quantity, we need to sum up the order quantities and divide by the number of rows. In this case, the mean is (100 + 50 + 25 + 75 + 10 + 20 + 15) / 7 = 42.14. Rounding to one decimal place, we get 42.1 as the mean of the order quantity.
質問 # 162
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