Google Professional-Data-Engineer日本語 Valid Q&A - in .pdf

  • Professional-Data-Engineer日本語 pdf
  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Oct 27, 2026
  • Q & A: 433 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
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Don't wait and enroll in these training courses offered by the official vendor that will help you ace the Professional Data Engineer exam with a good score. Once you take this exam and earn the prestigious Professional Data Engineer certification, you will get a chance to obtain a high-paying job and an amazing opportunity to work with the experts. Don't waste your time on other tasks and start preparing for this exam today. The more you practice the more you will get closer to success as a data analyst or data engineer. Moreover, it will polish your skills throughout and allow you to efficiently in well-reputed companies.

The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

For more info visit:

Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test

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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
  • Create ML models using BigQuery ML, APIs, and AutoML.
  • Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
  • Hire BigQuery and Cloud SQL for interactive data analysis

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

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

SectionWeightObjectives
Operationalizing machine learning models20%- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation
- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
Ensuring solution quality and reliability17%- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Selecting appropriate storage solutions
  • 2. Designing for scalability and elasticity
  • 3. Designing for reliability and fault tolerance
Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Ingesting data from various sources
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes

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

Question #1

生放送のテレビ番組で、視聴者に携帯電話を使って投票するよう呼びかけます。このイベントでは、3分間に大量のデータが生成されます。あなたは「投票インフラ」の責任者であり、プラットフォームが負荷を処理できること、およびすべての投票が処理されることを保証しなければなりません。投票中は部分的な結果を表示する必要があります。投票終了後は、コストを最適化しながら投票を一度だけ正確にカウントする必要があります。あなたは何をすべきでしょうか?

  • A. 高可用性(HA)構成でMemorystoreインスタンスを作成します。
  • B. 投票結果をPub/Subトピックに書き込み、データフローパイプラインを介してBigtableとBigQueryの両方にロードします。Bigtableでリアルタイムの結果を取得し、BigQueryで後日分析を行います。投票が終了したら、Bigtableインスタンスをシャットダウンします。
  • C. 高可用性 (HA) 構成と複数の読み取りレプリカを備えた Cloud SQL for PostgreSQL データベースを作成します。
  • D. Pub/Subトピックに投票を書き込み、Cloud Functionsがそれを購読してBigQueryに投票を書き込みます。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

数百万台のコンピューターの CPU とメモリの使用状況を時系列で保存するためのデータベースを選択する必要があります。このデータは 1 秒間隔のサンプルで保存する必要があります。アナリストはデータベースに対してリアルタイムのアドホック分析を実行します。クエリを実行するたびに課金されることを避け、スキーマ設計によってデータセットの将来の成長に対応できるようにする必要があります。どのデータベースとデータ モデルを選択すればよいでしょうか。

  • A. BigQuery にテーブルを作成し、CPU とメモリの新しいサンプルをテーブルに追加します。
  • B. BigQuery で幅の広いテーブルを作成し、毎秒のサンプル値の列を作成し、毎秒の間隔で行を更新します。
  • C. Cloud Bigtable に、コンピュータ ID と各分のサンプル時間を結合した行キーを持つワイド テーブルを作成し、各秒の値を列データとして結合します。
  • D. Cloud Bigtable に、Computer Engine のコンピュータ識別子と各秒のサンプル時間を組み合わせた行キーを持つ狭いテーブルを作成します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

2TB のリレーショナル データベースを Google Cloud Platform に移行する必要があります。このデータベースを使用するアプリケーションを大幅にリファクタリングするためのリソースがなく、運用コストが最大の懸念事項です。
データの保存と提供にはどのサービスを選択しますか?

  • A. クラウド ファイアストア
  • B. クラウド ビッグテーブル
  • C. クラウドSQL
  • D. クラウドスパナー
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #4

あなたは広告会社に勤務しており、広告ブロックのクリック率を予測する Spark ML モデルを開発しました。これまでオンプレミスのデータセンターで開発を行ってきましたが、会社が Google Cloud に移行することになりました。データセンターは間もなく閉鎖されるため、迅速なリフトアンドシフト移行が必要です。ただし、これまで使用してきたデータは BigQuery に移行されます。Spark ML モデルは定期的に再トレーニングしているため、既存のトレーニングパイプラインを Google Cloud に移行する必要があります。どうすればよいでしょうか?

  • A. Compute Engine 上に Spark クラスターを起動し、BigQuery からエクスポートされたデータで Spark ML モデルをトレーニングします。
  • B. 既存の Spark ML モデルのトレーニングには Cloud Dataproc を使用しますが、データは BigQuery から直接読み込むようにします。
  • C. TensorFlowでモデルを書き直し、Vertex AIの使用を開始しましょう。
  • D. 既存の Spark ML モデルのトレーニングに Cloud ML Engine を使用する
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #5

オンプレミス環境上のプラットフォームでは、毎日100GBのデータが生成され、そのデータは数百万個の構造化されたJSONテキストファイルで構成されています。オンプレミス環境はパブリックインターネットからアクセスできません。Google Cloud製品を使用してプラットフォームのデータを照会および探索したいと考えています。どうすればよいでしょうか?

  • A. クラウドスケジューラを使用して、オンプレミス環境からクラウドストレージにデータを毎日コピーします。
    BigQueryデータ転送サービスを使用して、データをBigQueryにインポートします。
  • B. 転送アプライアンスを使用して、オンプレミス環境からクラウドストレージにデータをコピーします。BigQueryデータ転送サービスを使用して、BigQueryにデータをインポートします。
  • C. BigQueryデータ転送サービスデータセットコピーを使用して、すべてのデータをBigQueryに転送します。
  • D. オンプレミス環境からクラウドストレージにデータをコピーするには、オンプレミスデータ転送サービスを使用します。BigQueryデータ転送サービスを使用して、BigQueryにデータをインポートします。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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