Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) : Professional-Data-Engineer日本語

  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Oct 11, 2026
  • Q & A: 433 Questions and Answers

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About Google Professional-Data-Engineer日本語 Exam Questions

A certification does not hand anyone a job — but it proves ability in a form employers recognize. The Professional-Data-Engineer日本語 credential carries that proof, and BraindumpsPrep carries the preparation: 433 practice questions with verified answers.

Google Professional-Data-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer Exam
Exam Number:Professional Data Engineer
Passing Score:Not publicly disclosed (scaled scoring system)
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Exam Price:$200 USD
Available Languages:Portuguese, French, Spanish, English, Japanese, German
Exam Format:multiple choice, case study, multiple select
Related Certifications:Google Cloud Certified - Associate Cloud Engineer
Google Cloud Certified - Professional Cloud Architect
Google Cloud Certified - Professional Data Analytics Engineer
Google Cloud Certified - Professional Machine Learning Engineer
Exam Duration:120 minutes
Certificate Validity Period:2 years
Recommended Training:Google Cloud Data Engineering Learning Path
Google Cloud Professional Data Engineer Course (Coursera)
Exam Registration:Google Cloud Certification Registration
Sample Questions:Free Download real Professional-Data-Engineer日本語 actual tests
Exam Way:Online proctored exam or onsite test center (Kryterion Webassessor)
Pre Condition:No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality28%- Security and governance
  • 1. Data governance and compliance
    • 2. IAM and access control in GCP
      - Reliability and performance
      • 1. Monitoring pipelines and workloads
        • 2. Fault tolerance and recovery strategies
          Designing data processing systems22%- Batch and streaming data processing design
          • 1. Event-driven vs batch architectures
            • 2. Latency, throughput, and consistency trade-offs
              - Data architecture and storage design
              • 1. Designing scalable and cost-effective data models
                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                  Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Vertex AI pipeline deployment
                    • 2. Feature engineering and feature stores
                      - Model deployment and monitoring
                      • 1. Online vs batch prediction
                        • 2. Model monitoring and drift detection
                          Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                            • 2. Streaming ingestion (Pub/Sub, Dataflow)
                              - Data processing and transformation
                              • 1. Using Dataproc, Dataflow, and BigQuery SQL
                                • 2. ETL/ELT pipeline design

                                  Professional-Data-Engineer日本語 Exam FAQ and Honest Answers

                                  Online proctored exam or onsite test center (Kryterion Webassessor) Register for the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam through these official channels:

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                                  BraindumpsPrep issues a full refund if you take the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.

                                  The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam requires Not publicly disclosed (scaled scoring system) to pass, and registration costs $200 USD. A failed attempt means paying the same fee again — a readiness check with the BraindumpsPrep engines before booking is the cheaper option.

                                  The official Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) outline defines 4 domains. The leading three are Operationalizing machine learning models (26%), Building and operationalizing data processing systems (24%), and Designing data processing systems (22%). The full list is in the topics section above, and BraindumpsPrep's 433 practice questions cover each one.

                                  The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam contains Approximately 50–60 multiple choice and multiple select questions to complete within 120 minutes. Timed simulation in the BraindumpsPrep Desktop Test Engine teaches that rhythm before exam day does.

                                  No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services Since requirements change over time, confirm the latest on the official Google exam page before scheduling.

                                  Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 433 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.

                                  The Professional-Data-Engineer日本語 exam is a Google certification exam validating the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) syllabus shown above. It belongs to these credential paths: Google Cloud Certified - Associate Cloud Engineer, Google Cloud Certified - Professional Cloud Architect, Google Cloud Certified - Professional Machine Learning Engineer, Google Cloud Certified - Professional Data Analytics Engineer. Earning it in 2026 demonstrates ability in a form employers recognize — and BraindumpsPrep prepares candidates with 433 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.

                                  Google recommends these training resources for the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam:

                                  Follow the training with steady self-testing — the 433 practice questions from BraindumpsPrep help you apply theory and fill any gaps the training leaves.

                                  Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Sample Questions:

                                  Question #1

                                  あなたの会社は最近急速に成長し、以前よりも大幅に高い速度でデータを取り込んでいます。あなたは Apache Hadoop で毎日のバッチ MapReduce 分析ジョブを管理しています。しかし、最近のデータの増加により、バッチ ジョブが遅れています。開発チームがコストを増やさずに分析の応答性を向上させる方法を提案するように求められました。あなたは開発チームに何を提案すべきでしょうか?

                                  • A. Apache Spark でジョブを書き換えます。
                                  • B. Hadoop クラスターのサイズを増やします。
                                  • C. Pig でジョブを書き換えます。
                                  • D. Hadoop クラスターのサイズを縮小しますが、Hive のジョブも書き換えます。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

                                  Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

                                  Question #2

                                  Cloud Bigtable 用の HBase Shell とは何ですか?

                                  • A. HBase シェルは、テーブルの作成や削除などの管理タスクを実行する GUI ベースのインターフェースです。
                                  • B. HBase シェルは、新しい仮想化インスタンスの作成や削除などの管理タスクを実行するハイパーバイザー ベースのシェルです。
                                  • C. HBase シェルは、Cloud Bigtable インスタンスへのアクセスを許可するためのユーザー アカウント管理機能のみを実行するコマンドライン ツールです。
                                  • D. HBase シェルは、テーブルの作成や削除などの管理タスクを実行するコマンドライン ツールです。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

                                  Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

                                  Question #3

                                  サードパーティから毎月 CSV 形式のデータ ファイルを受け取ります。このデータをクレンジングする必要がありますが、ファイルのスキーマは 3 か月ごとに変更されます。これらの変換を実装するための要件は次のとおりです。
                                  スケジュールに従って変換を実行する
                                  開発者以外のアナリストが変換を変更できるようにする
                                  変換を設計するためのグラフィカルツールの提供
                                  何をすべきでしょうか?

                                  • A. 各月のCSVデータをBigQueryに読み込み、SQLクエリを記述してデータを標準スキーマに変換します。変換されたテーブルをSQLクエリで結合します。
                                  • B. Cloud Dataproc 上の Apache Spark を使用して、データフレームを作成する前に CSV ファイルのスキーマを推測します。次に、データを Cloud Storage に書き出して BigQuery に読み込む前に、Spark SQL で変換を実装します。
                                  • C. アナリストが変換を実行するために Python で Cloud Dataflow パイプラインを記述するのを支援します。Python コードはリビジョン コントロール システムに保存し、受信データのスキーマが変更されると変更する必要があります。
                                  • D. Cloud Dataprep を使用して変換レシピを構築および維持し、スケジュールに従って実行します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

                                  Question #4

                                  BigQuery テーブルをデータ シンクとして使用したいと考えています。どの書き込みモードで BigQuery をシンクとして使用できますか?

                                  • A. BigQuery はシンクとして使用できません
                                  • B. バッチのみ
                                  • C. バッチとストリーミングの両方
                                  • D. ストリーミングのみ
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

                                  Explanation: Only visible for BraindumpsPrep members. You can sign-up / login (it's free).

                                  Question #5

                                  会社では、履歴データを Cloud Storage にアップロードする必要があります。セキュリティ ルールにより、外部 IP からオンプレミス リソースへのアクセスは許可されません。最初のアップロード後、既存のオンプレミス アプリケーションから毎日新しいデータが追加されます。どうすればよいでしょうか。

                                  • A. Cloud Dataflow を使用して、データを Cloud Storage に書き込みます。
                                  • B. Compute Engine VM に FTP サーバーをインストールして、ファイルを受信して​​ Cloud Storage に移動します。
                                  • C. オンプレミス サーバーから gsutil rsync を実行します。
                                  • D. データ転送を実行するためのジョブ テンプレートを Cloud Dataproc に記述します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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