Who should take the Google Professional Data Engineer exam
Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification
- Developers responsible for managing big data transformation initiatives
- Data analysts
- Data scientists
- Data engineers
- Data architects
- Business analysts
Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions
The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
Reference: https://cloud.google.com/certification/data-engineer
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Exam Topics
The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:
Topic 1. Designing Data Processing Systems
To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.
Prerequisites
There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.
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: Storing the data (~20% of the exam) | 20% | - Using a data lake
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Topic 4: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Topic 5: Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
|
Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) Sample Questions:
あなたの会社の経営者から銀行取引のデータベースを渡されました。各行には、ユーザー ID、取引タイプ、取引場所、取引金額が含まれています。彼らは、データに適用できる機械学習の種類を調べるように依頼しています。どの 3 つの機械学習アプリケーションを使用できますか? (3 つ選択してください。)
- A. 特徴の類似性に基づいてトランザクションを N 個のカテゴリに分割するクラスタリング。
- B. トランザクションの場所を予測するための教師なし学習。
- C. トランザクションの場所を予測するための強化学習。
- D. どのトランザクションが不正である可能性が高いかを判断するための教師あり学習。
- E. トランザクションの場所を予測するための教師あり学習。
- F. どのトランザクションが不正である可能性が高いかを判断するための教師なし学習。
Correct Answer: A,E,F 🗳️
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下の図に示すようなデータがあります。2つの次元はXとYで、各点の濃淡はそれがどのクラスに属するかを表しています。線形アルゴリズムを使用してこのデータを正確に分類したいと考えています。そのためには、合成特徴量を追加する必要があります。その特徴量の値はいくつにすべきでしょうか？
- A. X2+Y2
- B. cos(X)
- C. ×2
- D. Y2
Correct Answer: A 🗳️
貴社のデータアナリストチームは、2000スロットのスロット予約を持つGoogle Cloudプロジェクトで、アドホッククエリとスケジュールされたSQLパイプラインにBigQueryを使用しています。しかし、最近、時間制約のない新しいSQLパイプラインが数百個導入されたことで、チームは頻繁にクォータエラーに遭遇しています。ログを調べたところ、ピーク時には約1500のクエリが同時にトリガーされていることがわかりました。同時実行の問題を解決する必要があります。どうすればよいでしょうか？
- A. SQLパイプラインとアドホッククエリを対話型クエリジョブとして実行するように更新します。
- B. プロジェクトのスロット容量をベースライン0、最大予約サイズで増加します。
3000。 - C. SQLパイプラインをバッチクエリとして実行するように更新し、アドホッククエリを対話型クエリジョブとして実行します。
- D. プロジェクトのスロット容量をベースライン2000、最大予約サイズで増加します。
3000。
Correct Answer: C 🗳️
Google Cloud Bigtable スキーマを設計するときは、_________ することをお勧めします。
- A. リレーショナルデータベース設計に基づいたスキーマ設計を作成する
- B. 行間の原子性を必要とするスキーマ設計を作成する
- C. 行間の原子性を必要とするスキーマ設計を避ける
- D. NoSQL の概念に基づいたスキーマ設計を避ける
Correct Answer: C 🗳️
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あなたは画像認識分野のニッチな製品に取り組んでいます。あなたのチームは、チームが実装したカスタム C++ TensorFlow オペレーションが中心となるモデルを開発しました。これらのオペレーションはメインのトレーニング ループ内で使用され、大規模な行列乗算を実行します。現在、モデルのトレーニングには数日かかります。Google Cloud のアクセラレータを使用して、この時間を大幅に短縮し、コストを低く抑えたいと考えています。どうすればよいですか?
- A. CPU を維持し、モデルをトレーニングするクラスターのサイズを増やします。
- B. カスタム オペレーションに GPU カーネル サポートを実装した後、クラウド GPU を使用します。
- C. コードに追加の調整を加えずに Cloud TPU を使用します。
- D. カスタム オペレーションに GPU カーネル サポートを実装した後、Cloud TPU を使用します。
Correct Answer: B 🗳️
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