[2025年04月22日] 合格CompTIA Data+ DA0-001日本語試験問題集には312問があります
究極ガイドの無料準備CompTIA DA0-001日本語試験問題と解答
質問 # 29
メディア企業のデータ アナリストは、最も人気のある映画のジャンルを特定する必要があります。以下の表を考えると：
このタスクを完了する前に、ジャンル列に対して行う必要があるのは次のうちどれですか?
- A. マージ
- B. 区切り
- C. 連結
- D. 追加
正解：B
質問 # 30
データアナリストはチームと協力して、オンデマンド アクセスを必要とするクライアント向けのダッシュボードを作成しています。
クライアントの要件をサポートするのに最適な配信方法は次のうちどれですか?
- A. 予定
- B. 静的
- C. 電子メール
- D. 定期購入
正解：D
解説：
The best delivery method to support the client's requirement is C. Subscription.
Short explanation: A subscription is a delivery method that allows the client to access the dashboard on- demand, whenever they need it. A subscription can be set up by the data analyst or the client themselves, and it can be configured to send an email notification when the dashboard is updated or refreshed. A subscription also allows the client to view the dashboard online or download it as a file format of their choice12 A: Email is not the best delivery method because it does not allow the client to access the dashboard on- demand. Email deliveries are sent at a fixed time or frequency, and they may not reflect the latest data or changes in the dashboard. Email deliveries also have limitations on the file size and format of the dashboard attachments1 B: Scheduled is not the best delivery method because it does not allow the client to access the dashboard on- demand. Scheduled deliveries are similar to email deliveries, except that they are triggered by a specific event or condition, such as a data update or a threshold value. Scheduled deliveries also have the same limitations as email deliveries on the file size and format of the dashboard attachments1 D: Static is not the best delivery method because it does not allow the client to access the dashboard on- demand. Static deliveries are one-time deliveries that are manually generated by the data analyst or the client.
Static deliveries do not update or refresh automatically, and they may become outdated or irrelevant over time. Static deliveries also have limitations on the file size and format of the dashboard files3
質問 # 31
現在の日付は 2020 年 7 月 14 日です。データ アナリストは、同社の 2020 年第 2 四半期の売上を前年比で示すレポートを作成するように依頼されました。アナリストは次のレポートのうちどれを比較する必要がありますか?
- A. 2020 年第 2 四半期および 2019 年第 4 四半期
- B. 2020 年第 2 四半期および 2021 年第 2 四半期
- C. 2020 年初年度および 2019 年初年度
- D. 2020 年第 2 四半期および 2019 年第 2 四半期
正解：D
解説：
Explanation
To create a report that shows the company's year-over-year Q2 2020 sales, the analyst should compare the sales data from Q2 2020 and Q2 2019. Year-over-year (YoY) analysis is a method of comparing the performance of a business or a financial instrument over the same period in different years. It helps to identify trends, growth patterns, and seasonal fluctuations. Q2 refers to the second quarter of a year, which is usually from April to June. Therefore, the correct answer is C. References: YoY - Year over Year Analysis - Definition, Explanation & Examples, What is an Annual Sales Report: Definition, metrics, and tips - Snov.io
質問 # 32
次のうち、データの要素が母集団内の小さなサブグループのそれぞれからランダムに選択されるサンプリング方法を説明しているのはどれですか?
- A. 層別
- B. クラスター
- C. 単純ランダム
- D. システマティック
正解：A
解説：
This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:
* Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number.
* Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list.
* Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element.
Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.
質問 # 33
メディア企業のデータ アナリストは、最も人気のある映画のジャンルを特定する必要があります。以下の表を考えると：
このタスクを完了する前に、ジャンル列に対して行う必要があるのは次のうちどれですか?
- A. マージ
- B. 区切り
- C. 連結
- D. 追加
正解：B
解説：
Explanation
Delimiting is the process of splitting a column of data into multiple columns based on a separator or delimiter character. Delimiting can help separate data that is combined or concatenated in one column into distinct values or categories. For example, if a column contains text values that are separated by commas, such as
"Comedy, Suspense", delimiting can split this column into two columns, one for "Comedy" and one for
"Suspense". Delimiting is different from other options, such as appending, merging, or concatenating, which are methods of combining or joining data from multiple columns or sources. In this case, the data analyst needs to determine the most popular movie genre based on the Genre column in the table. However, this column contains multiple genres for each movie, separated by commas. Therefore, the data analyst must delimit this column before this task can be completed. Therefore, the correct answer is D. References: Split text into different columns with functions - Office Support, How to Split Text in Excel (Using Formulas & Split Function)
質問 # 34
JSON と XML の違いを最もよく説明しているものは次のうちどれですか?
- A. JSON は解析がはるかに困難です。
- B. JSON は終了タグを使用する必要があります。
- C. JSON の方が読み取りと書き込みが高速です。
- D. JSON 文字列が長くなります
正解：C
解説：
Explanation
The best answer is A. JSON is quicker to read and write.
JSON (JavaScript Object Notation) is a lightweight data-interchange format that is based on the JavaScript programming language and easy to understand and generate. JSON uses a simple syntax that consists of name-value pairs and arrays, and does not require any end tags or attributes. JSON is quicker to read and write than XML (Extensible Markup Language), which is a markup language that uses a tag structure to represent data items. XML has a more complex and verbose syntax that requires end tags, attributes, and namespaces123
質問 # 35
次のクエリ最適化手法のうち、特定のタスクに必要なデータのみを調べる必要があるのはどれですか?
- A. フラットファイルの作成
- B. 一時テーブルの作成
- C. ドキュメントのインデックス作成
- D. 実行計画の作成
正解：C
解説：
The correct answer is C. Indexing documents.
Indexing documents is a query optimization technique that involves creating a data structure that allows faster access to the data in the documents. Indexing documents can reduce the amount of data that needs to be scanned for a particular query, thus improving the performance and efficiency of the query. Indexing documents can also help with searching, sorting, filtering, and aggregating the data in the documents12
質問 # 36
データ アナリストは、オンライン マーケティング キャンペーンの結果をマーケティング マネージャーに提示する必要があります。マネージャーは、最も重要な KPI を確認し、マーケティングの投資収益率を測定したいと考えています。データ アナリストがこの情報をマネージャーに最もよく伝えるために使用する必要があるのは、次のうちどれですか?
- A. データ アナリストからの統計、結論、および推奨事項を含む概要
- B. 管理者が会社の年間予算実績を確認できる販売サービス ダッシュボード
- C. すべてのマーケティング キャンペーンとチャネルからの生データのスプレッドシート
- D. キャンペーンが開始された日のパフォーマンスを管理者が確認できるリアルタイム モニター
正解：A
解説：
The option that the data analyst should use to best communicate the information to the manager is a summary with statistics, conclusions, and recommendations from the data analyst. A summary is a concise and clear way of presenting the main findings and insights from the data analysis report. A summary should include relevant statistics that support the conclusions and recommendations from the data analyst. A summary should also highlight the most important KPIs and measure the return on marketing investment in relation to the objectives of the online marketing campaign. The other options are not as effective as using a summary to communicate the information to the manager, as they either provide too much or too little information or do not address the manager's needs or expectations. A real-time monitor may provide too much information that can be overwhelming or distracting for the manager who wants to see only the most important KPIs and measure the return on marketing investment. A self-service dashboard may provide too little information that can be insufficient or unclear for the manager who wants to see some guidance and interpretation from the data analyst. A spreadsheet of raw data may provide irrelevant or inaccurate information that can be confusing or misleading for the manager who wants to see some analysis and insights from the data analyst. Reference: [How to Write an Executive Summary for Your Data Analysis Report - Towards Data Science]
質問 # 37
Andy は小売業者の価格アナリストです。彼は仮説検定を使用して、電子クーポンを受け取った人が平均してより多くの買い物をするかどうかを評価したいと考えています。
アンディの帰無仮説はどうあるべきですか?
- A. 電子クーポンを受け取った人は、平均してより多くの買い物をしません。
- B. 電子クーポンを受け取る人は、平均して支出が少ない。
- C. 電子クーポンを受け取っていない人は、平均してより多くの買い物をします。
- D. 電子クーポンを受け取る人は、平均してより多くの買い物をします。
正解：A
解説：
Explanation
The null hypothesis presumes the status quo. Andy is testing whether or not people who receive an electronic coupon spend more on average, so, the null hypothesis states that people who receive the coupon do spend more on average.
質問 # 38
アナリストは毎日レポートを実行し、データを分析する前にデータポイントの数を検証する必要があります。データポイントの数は、前日からの合計数の約 20% ずつ毎日増加します。ある日のデータポイント数は 8,798 でした。翌日のデータポイントの総数は次のうちどれですか?
- A. 9,600
- B. 10,800
- C. 10,600
- D. 7,038
正解：C
解説：
This is because the number of datapoints increases each day by approximately 20% of the total number from the day before. Therefore, to find the number of datapoints on the next day, we can use the formula:
Plugging in the given values, we get:
Since we are dealing with whole numbers, we can round up the result to the nearest integer, which is 10,600.
質問 # 39
さまざまな人が一連の手書き調査を手動でオンライン データベースに入力します。このデータで最も発生する可能性が高い問題は次のうちどれですか? （2つ選んでください。）
- A. データ制約
- B. データ属性の制限
- C. データバイアス
- D. データ精度
- E. データ操作
- F. データの一貫性
正解：D、F
解説：
Explanation
Data accuracy refers to the extent to which the data is correct, reliable, and free of errors. When different people manually type a series of handwritten surveys into an online database, there is a high chance of human error, such as typos, misinterpretations, omissions, or duplications. These errors can affect the quality and validity of the data and lead to incorrect or misleading analysis and decisions.
Data consistency refers to the extent to which the data is uniform and compatible across different sources, formats, and systems. When different people manually type a series of handwritten surveys into an online database, there is a high chance of inconsistency, such as different spellings, abbreviations, formats, or standards. These inconsistencies can affect the integration and comparison of the data and lead to confusion or conflicts.
Therefore, to ensure data quality, it is important to have clear and consistent rules and procedures for data entry, validation, and verification. It is also advisable to use automated tools or methods to reduce human error and inconsistency.
質問 # 40
「データ ガバナンス」という用語を最も適切に説明しているのは次のうちどれですか?
- A. データ ガバナンスは、サイバー犯罪者によるデータ侵害から保護するポリシーです。
- B. データ ガバナンスは、組織内のデータ視覚化ダッシュボードの開発を管理します。
- C. データ ガバナンスは、組織内のデータを分析、操作、レポートするプロセスです。
- D. データ ガバナンスは、企業内のデータの可用性、使いやすさ、整合性、セキュリティです。
正解：D
解説：
Data governance refers to the overarching management of data's availability, usability, integrity, and security within an organization. It involves setting policies and standards that govern data usage, determining data ownership, implementing data security measures, and ensuring that data is accessible for business insights while maintaining its quality. The goal of data governance is to ensure that data is consistent, trustworthy, and not misused, supporting compliance with data privacy regulations and enabling effective data analytics to optimize operations and drive business decision-making.
References:
* Understanding Data Governance and Its Importance1.
* The Role of Data Governance in Data Management2.
* Defining Data Governance and Its Business Value3.
質問 # 41
アナリストは、米国の郊外の家族の収入データを扱っています。データセットには多くの外れ値があり、アナリストは典型的な収入を表すメジャーを提供する必要があります。次のうち、アナリストの目標を最もよく満たすのはどれですか?
- A. 標準偏差
- B. 中央値
- C. 平均
- D. モード
正解：B
質問 # 42
次のデータ操作手法のうち、論理関数の例はどれですか?
- A. ブール値
- B. 集計
- C. どこで
- D. 場合
正解：D
解説：
This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:
=IF (condition, value_if_true, value_if_false)
The other data manipulation techniques are not examples of logical functions. Here is why:
* WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:
* AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:
* BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is:
質問 # 43
次の記述統計的方法のうち、中心傾向の尺度はどれですか? （2つ選んでください。）
- A. モード
- B. 相関
- C. 最小
- D. 平均
- E. 分散
- F. 最大
正解：A、D
解説：
Mean and mode are measures of central tendency, which describe the typical or most common value in a distribution of data. Mean is the arithmetic average of all the values in a dataset, calculated by adding up all the values and dividing by the number of values. Mode is the most frequently occurring value in a dataset. Other measures of central tendency include median, which is the middle value when the data is sorted in ascending or descending order.
質問 # 44
次のうち、離散データ型の例はどれですか?
- A. 2.5mi (4km)
- B. 5 人の子供
- C. 10.7lbs (4.9kg)
- D. 8in (20cm)
正解：B
質問 # 45
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