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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

Reference: https://cloud.google.com/certification/data-engineer

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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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

SectionWeightObjectives
Topic 1: 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
          Topic 2: 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
                  Topic 3: Ensuring solution quality28%- Security and governance
                  • 1. Data governance and compliance
                    • 2. IAM and access control in GCP
                      - Reliability and performance
                      • 1. Fault tolerance and recovery strategies
                        • 2. Monitoring pipelines and workloads
                          Topic 4: 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

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

                                  地震データを分析するシステムを設計します
                                  a. 抽出、変換、ロード (ETL) プロセスは、Apache Hadoop クラスター上で一連の MapReduce ジョブとして実行されます。ETL プロセスでは、一部のステップの計算コストが高いため、データ セットの処理に数日かかります。その後、センサーの調整ステップが省略されていることがわかりました。今後、センサーの調整を体系的に実行するために、ETL プロセスをどのように変更すればよいでしょうか。

                                  • A. ETL プロセスの出力にセンサー キャリブレーション データを追加し、すべてのユーザーが自分でセンサー キャリブレーションを適用する必要があることを文書化します。
                                  • B. シミュレーションを通じてアルゴリズムを開発し、キャリブレーション係数に基づいて最後の MapReduce ジョブからのデータ出力の分散を予測し、すべてのデータに修正を適用します。
                                  • C. transformMapReduce ジョブを変更して、他の操作を実行する前にセンサーのキャリブレーションを適用します。
                                  • D. センサーのキャリブレーションを生データに適用するための新しい MapReduce ジョブを導入し、その後に他のすべての MapReduce ジョブが連鎖されるようにします。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

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                                  あなたは服のおすすめを行うモデルを構築しています。ユーザーのファッションは時間とともに変化する可能性が高いため、新しいデータが利用可能になった時点でモデルにストリーミングするデータパイプラインを構築します。このデータをどのように使用してモデルをトレーニングすべきでしょうか?

                                  • A. 既存データと新規データの組み合わせでモデルを継続的に再学習します。
                                  • B. 既存のデータをテストセットとして使用しながら、新しいデータでトレーニングを行います。
                                  • C. 新しいデータをテストセットとして使用しながら、既存のデータでトレーニングを行います。
                                  • D. 新しいデータのみを使用してモデルを継続的に再学習します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

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                                  Dataflow ジョブのネットワークを構成しています。データ パイプラインは、変換ロジックに必要なライブラリがプリインストールされたカスタム コンテナ イメージを使用します。データ パイプラインは、Cloud Storage からデータを読み取り、BigQuery にデータを書き込みます。パイプラインと Google API およびサービスの間で、コスト効率が高く安全な通信を確保する必要があります。何をすべきでしょうか。

                                  • A. ファイアウォール ルールを適用しながら、ワーカー VM に割り当てられた外部 IP アドレスをそのまま残します。
                                  • B. ファイアウォール ルールを適用しながら、Cloud NAT を有効にして送信インターネット接続を提供します。
                                  • C. 外部 IP アドレスを無効にし、プライベート サービス接続エンドポイント IP アドレスを確立します。
                                  • D. ワーカー VM からの外部 IP アドレスを無効にし、プライベート Google アクセスを有効にします。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

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                                  データ処理パイプラインを設計しています。パイプラインは、負荷の増加に応じて自動的に拡張できる必要があります。メッセージは少なくとも 1 回は処理され、1 時間以内に順序付けされる必要があります。ソリューションをどのように設計すればよいでしょうか。

                                  • A. メッセージの取り込みには Cloud Pub/Sub を使用し、ストリーミング分析には Cloud Dataflow を使用します。
                                  • B. メッセージの取り込みには Cloud Pub/Sub を使用し、ストリーミング分析には Cloud Dataproc を使用します。
                                  • C. メッセージの取り込みには Apache Kafka を使用し、ストリーミング分析には Cloud Dataflow を使用します。
                                  • D. メッセージの取り込みには Apache Kafka を使用し、ストリーミング分析には Cloud Dataproc を使用します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

                                  最高のパフォーマンスを得るために、Compute Engine インスタンスと Cloud Bigtable インスタンスに推奨されるゾーンは何ですか?

                                  • A. Compute Engine インスタンスと Cloud Bigtable インスタンスの両方を同じゾーンに配置します。
                                  • B. Compute Engine インスタンスと Cloud Bigtable インスタンスの両方を異なるゾーンに配置します。
                                  • C. Compute Engine インスタンスを Cloud Bigtable インスタンスから最も遠いゾーンに配置します。
                                  • D. Cloud Bigtable インスタンスを、データのすべてのコンシューマーと同じゾーンに配置します。
                                  Reveal Solution  Discussion  0

                                  Correct Answer: A  🗳️

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