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An outdated question bank is worse than none at all — it teaches yesterday's exam. In 2026, ActualTestsIT keeps the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) material aligned with the live Professional-Data-Engineer日本語 blueprint, with updates free for 365 days.

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
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Available Languages:English, Spanish, German, Japanese, French, Portuguese
Passing Score:Not publicly disclosed (scaled scoring system)
Exam Format:multiple select, case study, multiple choice
Related Certifications:Google Cloud Certified - Professional Data Analytics Engineer
Google Cloud Certified - Associate Cloud Engineer
Google Cloud Certified - Professional Machine Learning Engineer
Google Cloud Certified - Professional Cloud Architect
Exam Price:$200 USD
Certificate Validity Period:2 years
Exam Duration:120 minutes
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
          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. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                • 2. Designing scalable and cost-effective data models
                  Building and operationalizing data processing systems24%- Data processing and transformation
                  • 1. Using Dataproc, Dataflow, and BigQuery SQL
                    • 2. ETL/ELT pipeline design
                      - Data ingestion and integration
                      • 1. Streaming ingestion (Pub/Sub, Dataflow)
                        • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
                          Ensuring solution quality28%- Reliability and performance
                          • 1. Fault tolerance and recovery strategies
                            • 2. Monitoring pipelines and workloads
                              - Security and governance
                              • 1. Data governance and compliance
                                • 2. IAM and access control in GCP

                                  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 Operationalizing machine learning models (26%), Designing data processing systems (22%), 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 ActualTestsIT 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 ActualTestsIT 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. ActualTestsIT'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 ActualTestsIT'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:

                                  アナリティクス チームは、いくつかの異なる指標に基づいて、どの顧客があなたの会社と再び取引する可能性が最も高いかを判断するためのシンプルな統計モデルを構築したいと考えています。チームは、Google Cloud Storage に格納されているデータを使用して Apache Spark でモデルを実行したいと考えています。あなたは、このジョブを実行するために Google Cloud Dataproc を使用することを推奨しました。テストの結果、このワークロードは 15 ノードのクラスタで約 30 分で実行でき、結果を Google BigQuery に出力できることが分かりました。このワークロードを毎週実行する予定です。コストの観点からクラスタを最適化するにはどうすればよいでしょうか。

                                  • A. クラスタにプリエンプティブ仮想マシン (VM) を使用する
                                  • B. ジョブの実行速度を上げるために、メモリ容量の多いノードを使用する
                                  • C. ジョブの実行速度を上げるためにワーカーノードでSSDを使用する
                                  • D. ワークロードを Google Cloud Dataflow に移行する
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

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                                  データサイエンティストチームがオンプレミスのApache Hadoopソリューションに影響を与えることなくデータを探索できるように、Google Cloud上に分析環境を構築しました。オンプレミスのHadoop分散ファイルシステム(HDFS)クラスター内のデータは、Hiveパーティショニングの複数の列を持つ最適化行列(ORC)形式のファイルです。データサイエンティストチームは、オンプレミスのHDFSクラスターでHiveクエリエンジンのSQLを使用していたのと同様の方法でデータを探索できる必要があります。最もコスト効率の高いストレージおよび処理ソリューションを選択する必要があります。
                                  あなたはどうすべきですか?

                                  • A. データサイエンティストチームのために、ORCファイルをBigtableテーブルにインポートします。
                                  • B. ORC ファイルをクラウドストレージにコピーし、データサイエンティストチーム用に外部 BigQuery テーブルを作成します。
                                  • C. データサイエンティストチームのために、ORCファイルをBigQueryテーブルにインポートします。
                                  • D. ORC ファイルをクラウドストレージにコピーし、データサイエンティストチーム向けに Dataproc クラスターをデプロイします。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

                                  Google Cloud で実行されるマルチステップ データ パイプラインの実行を自動化したいと考えています。パイプラインには、相互に複数の依存関係を持つ Cloud Dataproc ジョブと Cloud Dataflow ジョブが含まれています。可能な場合はマネージド サービスを使用し、パイプラインは毎日実行します。どのツールを使用すればよいでしょうか。

                                  • A. Cloud Dataproc のワークフロー テンプレート
                                  • B. クラウド スケジューラ
                                  • C. クラウド コンポーザー
                                  • D. クローン
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

                                  Google の Dataflow SDK を使用して、以下に示すような顧客データを分析する予定です。プロジェクトの要件は、データ ソースから顧客名のみを抽出し、出力 PCollection に書き込むことです。
                                  トム、555 X 通り
                                  ティム、553 Yストリート
                                  サム、111 Z ストリート
                                  上記のデータ処理要件に最適な操作はどれですか?

                                  • A. データ抽出
                                  • B. シンクAPI
                                  • C. パルド
                                  • D. ソースAPI
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

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

                                  あなたは航空会社に勤務しており、BigQuery テーブルに気象データを保存する必要があります。気象データは機械学習モデルへの入力として使用されます。モデルは過去 30 日間の気象データのみを使用します。不要なデータの保存を避け、コストを最小限に抑えたいと考えています。どうすればよいでしょうか。

                                  • A. 天気日付の日時値でパーティション分割された BigQuery テーブルを作成します。パーティションの有効期限を 30 日に設定します。
                                  • B. 各レコードに取り込みタイムスタンプがある BigQuery テーブルを作成し、スケジュールされたクエリを実行して、取り込みタイムスタンプが 30 日以上経過したすべての行を削除します。
                                  • C. 天気データが参照する日の datetime 列を持つ BigQuery テーブルを作成します。スケジュールされたクエリを実行して、30 日以上前の datetime 値を持つ行を削除します。
                                  • D. 取り込み時間でパーティション分割された BigQuery テーブルを作成します。パーティションの有効期限を 30 日に設定します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

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

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