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Google Professional Data Engineer Certified Professional salary
The average salary of a Google Professional Data Engineer Certified Expert in
- United State - 151,247 USD
- England - 115,632 POUND
- Europe - 135,347 EURO
- India - 25,42,327 INR
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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:
- Introduction to Processing Streaming Data
- Prebuilt ML Models APIs for Unsaturated Data
- Cloud Dataflow Streaming Features
- Creating a Data Lake
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Building a Data Warehouse
- Serverless Messaging Using Cloud Sub/Pub
- Performing Spark on Cloud Dataproc
- Big Data Analytics with Cloud Al Platform Notebook
- Production ML Pipelines and use of Kubeflow
- Custom Model building Utilizing Cloud AutoML
- Introduction to Building Batch Data Pipelines
- Advanced BigQuery Performance and Functionality
- Introduction to Data Engineering
- Bigtable Streaming Features and High-Throughput BigQuery
- Custom Model building Using SQL in BigQuery ML
- Serverless Data Processing with Cloud Dataflow
These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.
Difficulty in Attempting Google Professional Data Engineer Exam Certification
If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer exam dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
| Topic 2: Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Topic 4: Storing the data (~20% of the exam) | 20% | - Designing for a data platform
|
| Topic 5: Ingesting and processing the data (~20% of the exam) | 20% | - Deploying and operationalizing the pipelines
|
Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Sample Questions:
Cloud Dataproc クラスタを使用する場合、ブラウザを ____ プロキシ経由で接続するように構成することで、YARN ウェブ インターフェースにアクセスできます。
- A. HTTP
- B. VPN
- C. HTTPS
- D. ソックス
Correct Answer: D 🗳️
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BigQuery テーブル内の特定のパーティションをクエリするにはどうすればよいでしょうか?
- A. WHERE句でDATE BETWEENを使用する
- B. WHERE句でDAY列を使用する
- C. EXTRACT(DAY)句を使用する
- D. WHERE句で__PARTITIONTIME疑似列を使用する
Correct Answer: D 🗳️
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開発チームと外部チームは、Visualization という名前のフォルダーにプロジェクト ビューアー ID およびアクセス管理 (IAM) ロールを持っています。開発チームには Cloud Storage と BigQuery の両方からデータを読み取れるようにしたいのですが、外部チームには BigQuery からのみデータを読み取れるようにしたいです。どうすればよいでしょうか?
- A. acme-raw-data protect 上に、外部チームの CIDR 範囲からのすべてのイングレス トラフィックを拒否する仮想プライベート クラウド (VPC) ファイアウォール ルールを作成します。
- B. 保護とクラウドストレージの両方を含む VPC サービス コントロール境界を作成し、制限付き API として設定します。開発チーム ユーザーを境界のアクセス レベルに追加します。
- C. acme-raw-dataプロジェクトの外部チームに対するクラウドストレージIAM権限を削除します。
- D. 保護と制限付き API としての BigQuery の両方を含む VPC サービス コントロールの境界を作成します。外部チーム ユーザーを境界のアクセス レベルに追加します。
Correct Answer: B 🗳️
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適切に設計された行キーを使用してデータを Cloud Bigtable に書き込むデータパイプラインがあります。このパイプラインを監視して、Cloud Bigtable クラスターのサイズを増やすタイミングを判断したいと考えています。これを実現するために実行できるアクションはどれですか?(2つ選択してください。)
- A. キービジュアライザーのメトリクスを確認します。書き込みプレッシャーインデックスが100を超えた場合は、クラウドBigtableクラスタのサイズを増やします。
- B. Key Visualizerのメトリクスを確認してください。読み取り負荷指数が100を超えた場合は、Cloud Bigtableクラスタのサイズを増やしてください。
- C. 読み取り操作のレイテンシを監視します。読み取り操作に 100 ms を超える時間がかかる場合は、Cloud Bigtable クラスタのサイズを増やしてください。
- D. 書き込み操作のレイテンシを監視します。書き込みレイテンシが継続的に増加している場合は、Cloud Bigtableクラスタのサイズを増やします。
- E. ストレージ使用率を監視します。使用率が最大容量の70%を超えた場合は、Cloud Bigtableクラスタのサイズを増やします。
Correct Answer: D,E 🗳️
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リージョン内の BigQuery データセットにデータを保存するための、耐障害性アーキテクチャを設計しています。過去 7 日以内に発生したテーブルの破損イベントからアプリケーションが復旧できることを保証する必要があります。RPO が最も低く、コスト効率が最も高いマネージド サービスを採用したいと考えています。どうすればよいでしょうか?
- A. BigQueryのタイムトラベル機能を使用して過去のデータにアクセスします。
- B. BigQueryテーブルのスナップショットを毎日作成します。
- C. BigQueryから破損したデータを除外した新しいテーブルにデータをエクスポートします。
- D. データをマルチリージョンBigQueryバケットに移行します。
Correct Answer: A 🗳️
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