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Last Updated: Oct 28, 2026

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A certification can reshape a career — new roles, wider circles, conversations with people who share your interests. That journey starts with preparation, and Exam4Docs handles it: 433 practice questions for the Professional-Data-Engineer日本語 exam, delivered in a minute.

Google Professional-Data-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer
Exam Number:Professional-Data-Engineer
Passing Score:Not officially published (estimated ~80%)
Real Exam Qty:50-60
Related Certifications:Google Cloud Certified Professional Data Engineer
Exam Format:Multiple-choice, Multiple-select
Exam Price:$200 USD
Exam Duration:120 minutes
Available Languages:Japanese, English
Certificate Validity Period:2 years
Sample Questions: DOWNLOAD DEMO
Exam Way:Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

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

SectionWeightObjectives
Topic 1: Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Defining architecture to support data access patterns
  • 3. Deciding the degree of data normalization
  • 4. Designing the data model
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Lifecycle management of data
  • 3. Planning for storage costs and performance
Topic 2: Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Scheduling jobs
  • 3. Workflow orchestration
Topic 3: Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Topic 4: Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Integrating with new data sources
  • 2. Data acquisition and import
  • 3. Processing logic
  • 4. AI data enrichment
  • 5. Batch processing
  • 6. Streaming (e.g., windowing, late arriving data)
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
- Designing data processing resources
  • 1. Cluster sizing and autoscaling
  • 2. Cost optimization
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
Topic 5: Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Data encryption
  • 2. Identity and Access Management (IAM)
  • 3. Auditing, privacy, and compliance
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases

Professional-Data-Engineer日本語 Exam FAQ and Buying Guide

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 Exam4Docs help you apply theory and fill any gaps the training leaves.

    The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam contains 50-60 to complete within 120 minutes. Timed simulation in the Exam4Docs Desktop Test Engine teaches that rhythm before exam day does.

    The official Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) outline defines 5 domains. The leading three are Designing data processing systems (~30% of the exam) (30%), Maintaining and automating data workloads (~15% of the exam) (15%), and Ingesting and processing the data (~20% of the exam) (20%). The full list is in the topics section above, and Exam4Docs's 433 practice questions cover each one.

    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 Professional Data Engineer. Earning the credential in 2026 can strengthen a career, and Exam4Docs prepares candidates with 433 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.

    Online (remote proctored) or at a testing center (Kryterion) Register for the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam through these official channels:

      After booking, Exam4Docs delivers your practice questions within 1 minute — preparation starts the same day.

      The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam requires Not officially published (estimated ~80%) to pass, and registration costs $200 USD. A failed attempt means paying the same fee again — a readiness check with the Exam4Docs engines before booking is the cheaper option.

      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. Download the demo first, experience the materials, and choose the format you like best — 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.

      Exam4Docs 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.

      No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. Since requirements change over time, confirm the latest on the official Google exam page before scheduling.

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

      Question #1

      あなたは、生データゾーンとキュレーション済みゾーンを含む Dataplex 環境を管理しています。データエンジニアリングチームが、キュレーション済みゾーンのバケットアセットに JSON および CSV ファイルをアップロードしていますが、これらのファイルは Dataplex によって自動的に検出されません。Dataplex によってファイルが検出されるようにするには、どうすればよいですか?

      • A. bgコマンドラインツールを使用して、JSONファイルとCSVファイルをBigQueryテーブルにロードします。
      • B. クラウドストレージ内のCSVファイルとJSONファイルへのオブジェクトレベルのアクセス権を付与します。
      • C. JSONファイルとCSVファイルをrawゾーンに移動します。
      • D. キュレーションされたゾーンのファイルの自動検出を有効にします。
      Reveal Solution  Discussion  0

      Correct Answer: C  🗳️

      Question #2

      貴社は、データ取り込みと配信を一元化するシステムを選定中です。要件を満たすために、メッセージングシステムとデータ統合システムを検討されています。主な要件は以下のとおりです。
      - トピック内の特定のオフセットにシークする機能、場合によっては戻る機能
      これまで収集されたすべてのデータの始まりまで
      - 数百のトピックにおけるパブリッシュ/サブスクライブセマンティクスをサポート
      キーごとの順序を保持する
      どのシステムを選ぶべきでしょうか?

      • A. Firebase Cloud Messaging
      • B. クラウドストレージ
      • C. Apache Kafka
      • D. データフロー
      Reveal Solution  Discussion  0

      Correct Answer: C  🗳️

      Explanation: Only visible for Exam4Docs members. You can sign-up / login (it's free).

      Question #3

      プライベートユーザーデータを含む機密プロジェクトに取り組んでいます
      a. 社内で作業を行うために、Google Cloud Platform にプロジェクトを設定しました。外部コンサルタントが、プロジェクトの Google Cloud Dataflow パイプラインで複雑な変換のコーディングを支援します。ユーザーのプライバシーをどのように維持すればよいでしょうか。

      • A. コンサルタントが別のプロジェクトで作業できるように、匿名化されたデータのサンプルを作成します。
      • B. コンサルタントにプロジェクトの閲覧者ロールを付与します。
      • C. サービス アカウントを作成し、コンサルタントがそのアカウントを使用してログオンできるようにします。
      • D. コンサルタントにプロジェクトの Cloud Dataflow 開発者ロールを付与します。
      Reveal Solution  Discussion  0

      Correct Answer: A  🗳️

      Question #4

      あなたはBigQuery ML上で、顧客が自社製品を購入する可能性を予測する線形回帰モデルを作成しています。このモデルでは、都市名という変数を主要な予測要素として使用しています。モデルをトレーニングして提供するには、データを列に整理する必要があります。予測可能な変数を維持しながら、最小限のコーディングでデータを準備したいと考えています。どうすればよいでしょうか?

      • A. TensorFlowを使用して、語彙リストを含むカテゴリ変数を作成します。語彙ファイルを作成し、それをモデルの一部としてBigQuery MLにアップロードします。
      • B. Cloud Data Fusionを使用して、各都市を1、2、3、4、または5とラベル付けされた地域に割り当て、その番号を使用してモデル内の都市を表します。
      • C. BigQueryで、都市情報を含む列を含まない新しいビューを作成します。
      • D. BigQueryのSQLを使用して、ワンホットエンコーディング方式でstate列を変換し、各都市をバイナリ値を持つ列にします。
      Reveal Solution  Discussion  0

      Correct Answer: D  🗳️

      Question #5

      Google Cloud 上で構造化データ用のバッチパイプラインを再構築したいと考えています。大規模なデータ変換には PySpark を使用していますが、パイプラインの実行に 12 時間以上かかっています。開発とパイプラインの実行時間を短縮するために、サーバーレスツールと SQL 構文を使用したいと考えています。生データは既に Cloud Storage に移行済みです。速度と処理要件を満たしながら、Google Cloud 上でパイプラインを構築するにはどうすればよいでしょうか?

      • A. データを Cloud SQL に取り込み、PySpark コマンドを SparkSQL クエリに変換してデータを変換し、BigQuery のフェデレーション クエリを使用して機械学習を行います。
      • B. クラウドストレージからBigQueryにデータを取り込み、PySparkコマンドをBigQuery SQLクエリに変換してデータを変換し、変換結果を新しいテーブルに書き込みます。
      • C. Apache Beam Python SDKを使用して変換パイプラインを構築し、データをBigQueryに書き込みます。
      • D. PySparkコマンドをSparkSQLクエリに変換してデータを変換し、その後、Dataproc上でパイプラインを実行してデータをBigQueryに書き込みます。
      Reveal Solution  Discussion  0

      Correct Answer: B  🗳️

      Explanation: Only visible for Exam4Docs members. You can sign-up / login (it's free).

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