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試験の準備方法-便利なProfessional-Data-Engineer最新な問題集試験-有難いProfessional-Data-Engineer関連資料

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競争力が激しい社会に当たり、我々Jpshikenは多くの受験生の中で大人気があるのは受験生の立場からGoogle Professional-Data-Engineer試験資料をリリースすることです。たとえば、ベストセラーのGoogle Professional-Data-Engineer問題集は過去のデータを分析して作成ます。ほんとんどお客様は我々JpshikenのGoogle Professional-Data-Engineer問題集を使用してから試験にうまく合格しましたのは弊社の試験資料の有効性と信頼性を説明できます。

Google Professional-Data-Engineer試験は、データエンジニアリングの分野における個人の知識と専門知識をテストするように設計されています。これは、Googleクラウドプラットフォームでデータ処理システムを設計、構築、および維持する能力を実証した専門家を認識しているGoogleが提供する認定です。この試験では、データの摂取と処理、ストレージとデータ分析、機械学習、データの視覚化など、幅広いトピックをカバーしています。

>> Professional-Data-Engineer最新な問題集 <<

有効的な Professional-Data-Engineer最新な問題集 & 保証するGoogle Professional-Data-Engineer 公認された試験の成功Professional-Data-Engineer関連資料

私たちJpshikenに知られているように、Professional-Data-Engineer認定は、急速な開発の世界の多くの現代人にとってますます重要になっています。 Professional-Data-Engineer認定が多くの人にとってそれほど重要なのはなぜですか？認定を取得することは、人々がより良い仕事をしたり、より多くの富を得たり、より高い社会的地位を得るなど、夢を実現するのに役立つからです。多くの人々は、Professional-Data-Engineer認定を正常に取得するのが困難です。また、試験の合格と認定の取得に問題がある場合は、Professional-Data-Engineerクイズ準備を使用する時が来たと思います。

Google Professional-Data-Engineer認定試験は、データエンジニアリングの分野における専門家の知識とスキルを検証する高度に認められた認定プログラムです。この認定は、データエンジニアがデータ処理システムを設計、構築、維持する能力を実証するだけでなく、パフォーマンスと費用対効果のためにトラブルシューティングと最適化を実証するように設計されています。認定試験では、データ処理システム、データストレージと管理、データ分析と機械学習、セキュリティとコンプライアンスなど、さまざまなトピックをカバーしています。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q280-Q285):

質問 # 280
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your dat a. You want to continue through the model development lifecycle. What should you do next?

A. Delineate what data will be used for testing and what will be used for training the model.
B. Use your model to run predictions on fresh customer input data.
C. Test and evaluate your model on your curated data to determine how well the model performs.
D. Monitor your model performance, and make any adjustments needed.

正解：A

解説：
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a production environment. Reference:: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle

質問 # 281
You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

A. Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.
B. Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.
C. Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
D. Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

正解：D

質問 # 282
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO.
You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects.
You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

A. Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
B. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
C. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.
D. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.

正解：A

解説：
* Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
* Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
* Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
* Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.

質問 # 283
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?

A. Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.
B. Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.
C. Create a sale3_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
D. Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

正解：C

解説：
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes.
Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load.
Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECT id, SUM(line_items.quantity * line_items.price) AS total_amount
FROM sales_transaction
GROUP BY id;
AI-generated code. Review and use carefully. More info on FAQ.
References:
* Use nested and repeated fields
* BigQuery explained: Working with joins, nested & repeated data
* Arrays in BigQuery - How to improve query performance and optimise storage

質問 # 284
Which of the following is not true about Dataflow pipelines?

A. Pipelines represent a data processing job
B. Pipelines represent a directed graph of steps
C. Pipelines can share data between instances
D. Pipelines are a set of operations

正解：C

解説：
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms Reference: https://cloud.google.com/dataflow/model/pipelines

質問 # 285
......

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ちなみに、Jpshiken Professional-Data-Engineerの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1hjIkt0ArLLlfdDO4N19C2q9c_tUWX5PK