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QSDA2024サンプル問題集 & QSDA2024トレーニング資料

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>> QSDA2024サンプル問題集 <<

QSDA2024トレーニング資料、QSDA2024試験対応

最近Qlik試験に参加する人が多くなっています。どのように試験を準備すべきですか？受験生たちはまず試験センターでQSDA2024認証試験に関する情報を了解してください。順調にQSDA2024試験に合格するために、我々の問題集で復習することができます。我々の問題集は的中率が高いですから、あなたのQSDA2024試験への復習に役立つことができます。

Qlik QSDA2024 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• データ変換: このセクションでは、特定の要件に基づいてデータ コンテンツを作成するデータ アナリストとデータ アーキテクトのスキルについて説明します。また、null データと空白データの処理と、データ ロード スクリプトの文書化についても説明します。

トピック 2

• 検証: このセクションでは、データ アナリストとデータ アーキテクトがスクリプトとデータを検証およびテストする方法についてテストします。特定のシナリオでデータの正確性と整合性を確保するための最適な方法を選択することに重点を置いています。

トピック 3

• データ モデル設計: このセクションでは、データ アナリストとデータ アーキテクトが各データ ソースから関連する測定値と属性を決定する能力をテストします。

トピック 4

• 要件の特定: このセクションでは、主要なビジネス要件を定義するデータ アナリストの能力を評価します。これには、関係者の特定、関連するメトリックの選択、必要な粒度と集約のレベルの決定などのタスクが含まれます。

トピック 5

• データ接続: この部分では、データ アナリストが必要なデータ ソースとコネクタを識別する方法を評価します。さまざまなデータ ソースへの接続を確立するための最も適切な方法を選択することに重点を置いています。

 

Qlik Sense Data Architect Certification Exam - 2024 認定 QSDA2024 試験問題 (Q16-Q21):

質問 # 16
Refer to the exhibit.

A system creates log files and csv files daily and places these files in a folder. The log files are named automatically by the source system and change regularly. All csv files must be loaded into Qlik Sense for analysis.
Which method should be used to meet the requirements?

• A.
• B.
• C.
• D.

正解：A

解説：
In the scenario described, the goal is to load all CSV files from a directory into Qlik Sense, while ignoring the log files that are also present in the same directory. The correct approach should allow for dynamic file loading without needing to manually specify each file name, especially since the log files change regularly.
Here's whyOption Bis the correct choice:
* Option A:This method involves manually specifying a list of files (Day1, Day2, Day3) and then iterating through them to load each one. While this method would work, it requires knowing the exact file names in advance, which is not practical given that new files are added regularly. Also, it doesn't handle dynamic file name changes or new files added to the folder automatically.
* Option B:This approach uses a wildcard (*) in the file path, which tells Qlik Sense to load all files matching the pattern (in this case, all CSV files in the directory). Since the csv file extension is explicitly specified, only the CSV files will be loaded, and the log files will be ignored. This method is efficient and handles the dynamic nature of the file names without needing manual updates to the script.
* Option C:This option is similar to Option B but targets text files (txt) instead of CSV files. Since the requirement is to load CSV files, this option would not meet the needs.
* Option D:This option uses a more complex approach with filelist() and a loop, which could work, but it's more complex than necessary. Option B achieves the same result more simply and directly.
Therefore,Option Bis the most efficient and straightforward solution, dynamically loading all CSV files from the specified directory while ignoring the log files, as required.

 

質問 # 17

Refer to the exhibits.
On executing a load script of an app, the country field needs to be normalized. The developer uses a mapping table to address the issue. The script runs successfully but the resulting table is not correct.
What should the data architect do?

• A. Use a LEFT JOIN Instead of the APPLYMAP
• B. Use LOAD DISTINCT on the mapping table
• C. Review the values of the source mapping table
• D. Create two different mapping tables

正解：C

解説：
In this scenario, the issue arises from using the applymap() function to normalize the country field values, but the result is incorrect. The reason is most likely related to the values in the source mapping table not matching the values in the Fact_Table properly.
The applymap() function in Qlik Sense is designed to map one field to another using a mapping table. If the source values in the mapping table are inconsistent or incorrect, the applymap() will not function as expected, leading to incorrect results.
Steps to resolve:
* Review the mapping table (MAP_COUNTRY): The country field in the CountryTable contains values such as "U.S.", "US", and "United States" for the same country. To correctly normalize the country names, you need to ensure that all variations of a country's name are consistently mapped to a single value (e.g., "USA").
* Apply Mapping: Review and clean up the mapping table so that all possible variants of a country are correctly mapped to the desired normalized value.
Key References:
* Mapping Tables in Qlik Sense: Mapping tables allow you to substitute field values with mapped values. Any mismatches or variations in source values should be thoroughly reviewed.
* Applymap() Function: This function takes a mapping table and applies it to substitute a field value with its mapped equivalent. If the mapped values are not correct or incomplete, the output will not be as expected.

 

質問 # 18
Exhibit.

Refer to the exhibit.
A data architect is provided with five tables. One table has Sales Information. The other four tables provide attributes that the end user will group and filter by.
There is only one Sales Person in each Region and only one Region per Customer.
Which data model is the most optimal for use in this situation?

• A.
• B.
• C.
• D.

正解：C

解説：
In the given scenario, where the data architect is provided with five tables, the goal is to design the most optimal data model for use in Qlik Sense. The key considerations here are to ensure a proper star schema, minimize redundancy, and ensure clear and efficient relationships among the tables.
Option Dis the most optimal model for the following reasons:
* Star Schema Design:
* In Option D, the Fact_Gross_Sales table is clearly defined as the central fact table, while the other tables (Dim_SalesOrg, Dim_Item, Dim_Region, Dim_Customer) serve as dimension tables.
This layout adheres to the star schema model, which is generally recommended in Qlik Sense for performance and simplicity.
* Minimization of Redundancies:
* In this model, each dimension table is only connected directly to the fact table, and there are no unnecessary joins between dimension tables. This minimizes the chances of redundant data and ensures that each dimension is only represented once, linked through a unique key to the fact table.
* Clear and Efficient Relationships:
* Option D ensures that there is no ambiguity in the relationships between tables. Each key field (like Customer ID, SalesID, RegionID, ItemID) is clearly linked between the dimension and fact tables, making it easy for Qlik Sense to optimize queries and for users to perform accurate aggregations and analysis.
* Hierarchical Relationships and Data Integrity:
* This model effectively represents the hierarchical relationships inherent in the data. For example, each customer belongs to a region, each salesperson is associated with a sales organization, and each sales transaction involves an item. By structuring the data in this way, Option D maintains the integrity of these relationships.
* Flexibility for Analysis:
* The model allows users to group and filter data efficiently by different attributes (such as salesperson, region, customer, and item). Because the dimensions are not interlinked directly with each other but only through the fact table, this setup allows for more flexibility in creating visualizations and filtering data in Qlik Sense.
References:
* Qlik Sense Best Practices: Adhering to star schema designs in Qlik Sense helps in simplifying the data model, which is crucial for performance optimization and ease of use.
* Data Modeling Guidelines: The star schema is recommended over snowflake schema for its simplicity and performance benefits in Qlik Sense, particularly in scenarios where clear relationships are essential for the integrity and accuracy of the analysis.

 

質問 # 19
Exhibit.

Refer to the exhibit.
The data architect needs to build a model that contains Sales and Budget data for each customer. Some customers have Sales without a Budget, and other customers have a Budget with no Sales.
During loading, the data architect resolves a synthetic key by creating the composite key.
For validation, the data architect creates a table that contains Customer, Month, Sales, and Budget columns.
What will the data architect see when selecting a month?

• A. All Customer Names for both Sales and Budget records for the selected month
• B. Customer Names and Sales records for the selected month but with only non-null values in Budget column
• C. Customer Names and Budaets records for the selected month. Sales column can contain null or non-null values
• D. Customer Names and Sales records for the selected month, Budgets column can contain null or non-null values

正解：D

解説：
In the scenario where the data model is built with a composite key (keyYearMonthCustNo) to resolve synthetic keys, the following outcomes occur:
* Sales and Budget Data Integration:
* The composite key ensures that each combination of Year, Month, and Customer is uniquely represented in the combined Sales and Budget data.
* During data selection (e.g., when a specific month is selected), Qlik Sense will show all the customer names that have either Sales or Budget data associated with that month.
* Resulting Data View:
* For the selected month, customers with sales records will display their Sales data. However, if the corresponding Budget data is missing, the Budget column will contain null values.
* Similarly, if a customer has a Budget but no Sales data for the selected month, the Sales column will show null values.
Validation Outcome:When the data architect selects a month, they will see the following:
* Customer Names and Sales recordsfor the selected month, where the Sales column will have values and the Budget column may contain null or non-null values depending on the data availability.

 

質問 # 20
A company needs to analyze daily sales data from different countries. They also need to measure customer satisfaction of products as reported on a social media website. Thirty (30) reports must be produced with an average of 20,000 rows each. This process is estimated to take about 3 hours.
Which option should the data architect use to build this solution?

• A. Microsoft SQL Server
• B. Qlik REST Connector
• C. Qlik GeoAnalytics
• D. Mailbox IMAP

正解：B

解説：
In this scenario, the company needs to analyze daily sales data from different countries and also measure customer satisfaction of products as reported on a social media website. This suggests that the data is likely coming from different sources, including possibly an API or a web service (social media website).
TheQlik REST Connectoris the appropriate tool for this job. It allows you to connect to RESTful web services and retrieve data directly into Qlik Sense. This is especially useful for integrating data from various online sources, such as social media platforms, which typically expose data via REST APIs. The REST Connector enables the extraction of large datasets from these sources, which is necessary given the requirement to produce 30 reports with an average of 20,000 rows each.
* Microsoft SQL Serveris not suitable for fetching data from web services or social media platforms.
* Qlik GeoAnalyticsis used for mapping and geographical data visualization, not for connecting to RESTful services.
* Mailbox IMAPis for connecting to email servers and is not applicable to the data extraction needs described here.
Thus,Qlik REST Connectoris the correct answer for this scenario.

 

質問 # 21
......

多くの人は、QlikインターネットでQSDA2024学習準備を購入するとプライバシーが明らかになることを心配することがよくあります。 一部の人々は、一部のWebサイトQlik Sense Data Architect Certification Exam - 2024で製品を購入した後、匿名のSMS広告やテレマーケティングに悩まされることがよくあります。 しかし、プラットフォームでQSDA2024テスト資料を購入すると、このような状況Qlik Sense Data Architect Certification Exam - 2024は決して起こりません。 ここでは、顧客のプライバシーと購入情報をしっかりと保護し、顧客情報の開示は行わないことを厳soleに約束します。 QSDA2024準備トレントをQSDA2024購入すると、購入情報を入力するPass4Test専任の営業担当者がいます。 取引終了後、すべての顧客情報を保持および破棄する専門スタッフもいます。

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