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Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
Reference: https://cloud.google.com/certification/data-engineer
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The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
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You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
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Build & Operationalize Data Processing Systems
- Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
- Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
- Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Designing data processing systems | 20% | - Designing for business requirements
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Sample Questions:
オンプレミスのデータ ウェアハウスを BigQuery に移行しています。移行の一環として、チーム間のコラボレーションを促進して、組織のデータから最大限の価値を引き出したいと考えています。組織内のチームが読み取り専用データをセルフサービス方式で安全に公開、検出、サブスクライブできるアーキテクチャを設計する必要があります。コストを最小限に抑えながら、データの鮮度を最大限に高める必要があります。どうすればよいでしょうか。
- A. 各チームのプロジェクトで共有するための新しいデータセットを作成します。サブスクライブしているチームにデータセットに対する bigquery.dataViewer ロールを付与します。
- B. 承認されたデータセットを作成して、サブスクライブ チームのプロジェクトで共有データを公開します。
- C. BigQuery Data Transfer Service を使用して、データセットを一元化された BigQuery プロジェクトにコピーし、共有します。
- D. Analytics Hub を使用してデータ共有を容易にします。
Correct Answer: D 🗳️
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特定のテーブルから BigQuery データを 1 日に複数回確認する必要があります。クエリを実行する基になるテーブルのサイズは数ペタバイトですが、データをフィルタリングして、下流のユーザーにシンプルな集計を提供したいと考えています。クエリをより高速に実行し、最新の分析情報をより早く取得したいと考えています。どうすればよいでしょうか。
- A. スケジュールされたクエリを実行して、毎日特定の間隔で必要なデータを取得します。
- B. 最終結果で取得されるクエリ列を制限します。
- C. 実行中のクエリに基づいてマテリアライズド ビューを作成します。
- D. キャッシュされたクエリを使用して、結果までの時間を短縮します。
Correct Answer: C 🗳️
Dataflow プログラムをローカルで実行するために使用できる Java SDK クラスはどれですか?
- A. ローカルパイプラインランナー
- B. ローカルランナー
- C. マシンランナー
- D. ダイレクトパイプラインランナー
Correct Answer: D 🗳️
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組織では、6 か月間 Google BigQuery でデータを収集し、分析してきました。分析されたデータの大部分は、events_partitioned という時間パーティション テーブルに格納されています。クエリのコストを削減するために、組織では events というビューを作成しました。このビューは、過去 14 日間のデータのみをクエリします。このビューは、レガシー SQL で記述されています。来月、既存のアプリケーションは BigQuery に接続し、ODBC 接続を介してイベント データを読み取ることになります。アプリケーションが接続できることを確認する必要があります。実行する必要があるアクションはどれですか (2 つ選択してください)。
- A. 標準SQLを使用してイベントの新しいビューを作成する
- B. 認証に使用するODBC接続のサービスアカウントを作成します
- C. 標準SQLクエリを使用して新しいパーティションテーブルを作成します
- D. ODBC 接続と共有「イベント」用の Google Cloud Identity and Access Management(Cloud IAM)ロールを作成します。
- E. 標準SQLを使用してevents_partitionedの新しいビューを作成します。
Correct Answer: B,E 🗳️
現在、あなたの会社ではキャンペーン用のデータ パイプラインを設定しています。すべての Google Cloud Pub/Sub ストリーミング データについて、キャンペーン中に定期的に入力とそのタイミングを特定できることが重要なビジネス要件の 1 つです。エンジニアは、この目的のために Google Cloud Dataflow のウィンドウ処理と変換を使用することにしました。ただし、この機能をテストすると、すべてのストリーミング挿入で Cloud Dataflow ジョブが失敗することがわかりました。この問題の原因として最も可能性が高いのは何でしょうか。
- A. グローバルウィンドウ関数を適用していないため、パイプラインの作成時にジョブが失敗します。
- B. 非グローバルウィンドウ関数を適用していないため、パイプラインの作成時にジョブが失敗します。
- C. タイムスタンプが割り当てられていないため、ジョブが失敗します
- D. 遅れて入ってくるデータに対応するトリガーが設定されていないため、ジョブが失敗する
Correct Answer: B 🗳️
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