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Last Updated: Oct 28, 2026

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Every Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) retake costs the full registration fee plus a chunk of your momentum. In 2026, SurePassExams's Professional-Data-Engineer日本語 question bank — 433 expert-verified items — remains the efficient insurance policy against both.

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
Related Certifications:Google Cloud Certified - Professional Data Analytics Engineer
Google Cloud Certified - Professional Cloud Architect
Google Cloud Certified - Associate Cloud Engineer
Google Cloud Certified - Professional Machine Learning Engineer
Available Languages:Spanish, Portuguese, English, French, Japanese, German
Exam Duration:120 minutes
Exam Price:$200 USD
Passing Score:Not publicly disclosed (scaled scoring system)
Certificate Validity Period:2 years
Exam Format:case study, multiple choice, multiple select
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Recommended Training:Google Cloud Data Engineering Learning Path
Google Cloud Professional Data Engineer Course (Coursera)
Exam Registration:Google Cloud Certification Registration
Sample Questions: DOWNLOAD DEMO
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
Operationalizing machine learning models26%- ML pipeline integration
  • 1. Feature engineering and feature stores
    • 2. Vertex AI pipeline deployment
      - Model deployment and monitoring
      • 1. Online vs batch prediction
        • 2. Model monitoring and drift detection
          Ensuring solution quality28%- Reliability and performance
          • 1. Fault tolerance and recovery strategies
            • 2. Monitoring pipelines and workloads
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  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)
                          Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Streaming ingestion (Pub/Sub, Dataflow)
                            • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
                              - Data processing and transformation
                              • 1. ETL/ELT pipeline design
                                • 2. Using Dataproc, Dataflow, and BigQuery SQL

                                  Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Exam: Your Questions, Our Answers

                                  The Professional-Data-Engineer日本語 exam is Google's official assessment for the Google Cloud Certified - Professional Data Engineer certification, which sits at the Professional level. It verifies that you can work with the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) skill set the way real jobs demand — not merely recognize the terminology. It also belongs to a certification family that includes 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, so passing it strengthens your position across multiple career paths. For newcomers and veterans alike, the Professional-Data-Engineer日本語 exam is the kind of credential that converts effort into evidence.

                                  The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) blueprint contains 4 domains, beginning with Designing data processing systems (22%), Operationalizing machine learning models (26%), and Building and operationalizing data processing systems (24%). Use the weightings like a map of where the points live — the heaviest domains earn the largest share of your study hours. The complete topic list is in the exam topics section above; study against it, and nothing in the exam will feel unannounced.

                                  You're looking at Approximately 50–60 multiple choice and multiple select questions questions inside 120 minutes on the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam. Do the division now and memorize your per-question budget; on exam day, anything that blows its budget gets flagged and revisited with leftover time. Timed practice is what makes this instinctive — the SurePassExams PC test engine runs time-limited mock exams and scores them automatically, so you arrive already knowing your pace.

                                  Google sets the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) passing score at Not publicly disclosed (scaled scoring system), and each attempt costs $200 USD — full price every time, retakes included. That makes preparation the cheaper strategy by a wide margin. The practical path: take auto-scored timed mocks with SurePassExams practice questions until you clear the passing mark comfortably across multiple runs, then register with confidence grounded in data.

                                  The official Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) format list includes: multiple choice, multiple select, case study. Each format rewards a specific skill — scenario items reward careful reading order, multi-select items reward completeness — so practice should cover all of them, not just your favorites. SurePassExams's 433 practice questions span the full list, which means the exam's mechanics feel rehearsed and only its content requires thought.

                                  Google offers the Professional-Data-Engineer日本語 exam in English, Japanese, Spanish, Portuguese, French, German. Choose the language you process fastest when the clock is running — comprehension speed quietly decides borderline results. If English is your pick, working through SurePassExams's English Professional-Data-Engineer日本語 practice questions builds precisely the vocabulary the exam will use against you.

                                  Passing the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam earns a credential valid for 2 years. Record the expiry date as soon as you pass, and look into recertification options well in advance — a renewal planned early is trivial, a lapsed credential is not. Recertification policy belongs to Google and changes periodically, so confirm the current rules on the official certification page.

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

                                  Question #1

                                  パソコンのウェブブラウザから Cloud Dataproc クラスタにウェブ トラフィック データを安全に転送するには、_____ を使用する必要があります。

                                  • A. VPN接続
                                  • B. FTP接続
                                  • C. SSHトンネル
                                  • D. 特別なブラウザ
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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                                  Question #2

                                  3 つのデータ処理ジョブを開発しました。1 つは、Cloud Storage にアップロードされたデータを変換し、結果を BigQuery に書き込む Cloud Dataflow パイプラインを実行します。2 つ目は、オンプレミス サーバーからデータを取り込んで Cloud Storage にアップロードします。3 つ目は、サードパーティのデータ プロバイダから情報を取得して Cloud Storage にアップロードする Cloud Dataflow パイプラインです。これら 3 つのワークフローの実行をスケジュールして監視し、必要に応じて手動で実行できる必要があります。どうすればよいでしょうか。

                                  • A. Cloud Composer で直接非巡回グラフを作成し、ジョブをスケジュールしてモニタリングします。
                                  • B. Compute Engine インスタンスで cron ジョブを設定し、GCP API 呼び出しを使用してパイプラインをスケジュールおよび監視します。
                                  • C. Stackdriver Monitoring を使用して、Webhook 通知でアラートを設定し、ジョブをトリガーします。
                                  • D. GCP API 呼び出しを使用してジョブのスケジュールとステータスを要求する App Engine アプリケーションを開発します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

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                                  Question #3

                                  プリミティブ ロールを使用して実行できないのは次のうちどれですか。

                                  • A. プロジェクト内のすべてのデータセットに対して、GroupA に所有者アクセス権を、GroupB に編集者アクセス権を付与します。
                                  • B. プロジェクト内のすべてのデータセットに対して、UserA に所有者アクセス権を、UserB に編集者アクセス権を付与します。
                                  • C. ユーザーにプロジェクト内のすべてのデータセットを表示するアクセス権を付与しますが、それらに対してクエリを実行する権限は付与しません。
                                  • D. ユーザーに BigQuery への閲覧者アクセス権と Google Compute Engine インスタンスへの所有者アクセス権を付与します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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                                  Question #4

                                  BigQuery の静的参照データを使用して Cloud Pub/Sub のデータを強化する Apache Beam パイプラインを設計しています。 参照データは、単一のワーカーのメモリに収まるほど小さいです。 パイプラインは、分析のために強化された結果を BigQuery に書き込む必要があります。 このパイプラインでは、どのジョブタイプと変換を使用する必要がありますか。

                                  • A. バッチジョブ、PubSubIO、サイド入力
                                  • B. ストリーミング ジョブ、PubSubIO、BigQueryIO、副入力
                                  • C. ストリーミングジョブ、PubSubIO、JdbcIO、サイド出力
                                  • D. ストリーミング ジョブ、PubSubIO、BigQueryIO、副出力
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

                                  Question #5

                                  人間の顔が含まれているかどうかがラベル付けされた画像のデータセットがあるとします。このラベル付けされたデータセットを使用して画像内の人間の顔を認識するニューラル ネットワークを作成するには、どのようなアプローチが最も効果的でしょうか。

                                  • A. ピクセルの入力層、隠し層、および 2 つのカテゴリの出力層を持つニューラル ネットワークを構築します。
                                  • B. K-means クラスタリングを使用してピクセル内の顔を検出します。
                                  • C. 特徴エンジニアリングを使用して、入力データに目、鼻、口の特徴を追加します。
                                  • D. 複数の隠し層を持つニューラル ネットワークを作成してディープラーニングを使用し、顔の特徴を自動的に検出します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

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