Google Professional-Data-Engineer日本語 Actual PDF : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Google Professional-Data-Engineer日本語 Actual PDF
  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Aug 29, 2026
  • Q & A: 433 Questions and Answers
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About Google Professional-Data-Engineer日本語 Actual Exam

Facing the Professional-Data-Engineer日本語 exam without confidence usually means facing it without rehearsal. The 433 Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) practice questions at ActualPDF replace uncertainty with repetition, and in 2026 that remains the reliable formula.

Google Professional-Data-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer
Exam Number:Professional-Data-Engineer
Passing Score:Not officially published (estimated ~80%)
Certificate Validity Period:2 years
Real Exam Qty:50-60
Exam Price:$200 USD
Related Certifications:Google Cloud Certified Professional Data Engineer
Exam Duration:120 minutes
Exam Format:Multiple-select, Multiple-choice
Available Languages:Japanese, English
Sample Questions:Free Download Pass Professional-Data-Engineer日本語 Exam Cram
Exam Way:Online (remote proctored) or at a testing center (Kryterion)
Pre Condition:No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

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

SectionWeightObjectives
Preparing and using data for analysis (~15% of the exam)15%- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Deciding the degree of data normalization
  • 3. Mapping business requirements
  • 4. Designing the data model
- Designing for a data platform
  • 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
  • 2. Building a federated governance model for distributed data systems
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Lifecycle management of data
  • 3. Analyzing data access patterns
Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Workflow orchestration
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Scheduling jobs
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cluster sizing and autoscaling
  • 3. Cost optimization
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
- Designing data pipelines
  • 1. Data acquisition and import
  • 2. AI data enrichment
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Processing logic
  • 5. Integrating with new data sources
  • 6. Batch processing

Questions and Answers About Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) is an official Google Cloud exam, listed under exam code Professional-Data-Engineer日本語. A passing result earns you the Google Cloud Certified certification at the Professional level. It also ties into Google Cloud Certified Professional Data Engineer, extending its value across your certification roadmap. Employers read this credential as verified competence, which is why it keeps appearing in job requirements.

Expect 50-60 questions inside 120 minutes on the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam. That pace punishes hesitation, so rehearse it: the ActualPDF software engine simulates the real exam scene, reminds you of the questions you got wrong, and pushes you to re-practice them until the clock stops being your enemy.

Passing Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) requires Not officially published (estimated ~80%), and the official registration fee is $200 USD. Retakes charge the full $200 USD again, which is why experienced candidates treat preparation as the cheaper exam fee. Verify your readiness with repeated ActualPDF practice scores above the requirement before you commit to a date.

No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.

Requirements evolve, so confirm the current conditions before registering on the official exam page.

Yes. ActualPDF provides a free download demo of the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) material, so you can check the content before choosing a version. After purchase, a one-year warranty covers you: the latest version is sent to you as it releases, free for 365 days, and after expiry you can extend the update service at a 50% discount.

Your purchase is covered by a 100% money-back guarantee with clear conditions. Take the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam within 60 days of purchase; if you fail, provide your unqualified result by submitting a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and the full refund is processed within 7 days. The exam must match your product, candidate and payer names must match, and attempts within 3 days of purchase, unused downloads, free materials, and expired orders are not covered. Alternatively, exchange for two other exam products of equal value, free, or wait for updates while keeping your original product's update service.

Delivery is instant: files unlock for download at payment and are emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service, which works 7/24 and normally replies within two hours. Installation is unlimited across your computers.

The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) syllabus spans 5 domains, led by Designing data processing systems (~30% of the exam) (30%), Ingesting and processing the data (~20% of the exam) (20%), and Storing the data (~20% of the exam) (20%). The complete topic list is published above; candidates who study the map first rarely get lost later.

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

Question 1

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. 既存の Spark ML モデルのトレーニングに Cloud ML Engine を使用する
B. 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。
C. 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。
D. TensorFlowでモデルを書き直し、Vertex AIの使用を開始しましょう。


Question 2

Dataproc クラスタ インスタンス上のソフトウェアをカスタマイズする方法ではないものはどれですか。

A. マスターノードにログインし、そこから変更を加える
B. 初期化アクションを設定する
C. Cloud Deployment Manager を使用してクラスタを構成する
D. クラスタープロパティを使用して構成ファイルを変更する


Question 3

Bigtable の時系列データでホットスポットを回避するために推奨される方法はどれですか?

A. フィールドの昇格
B. 塩漬け
C. ランダム化
D. ハッシュ


Question 4

ユーザーのクリックストリームデータを分析して、コンテンツのおすすめをパーソナライズします。データは継続的に送信されるため、セッション化(一定時間内のユーザーごとにクリックをグループ化する)やユーザーアクティビティの集計などの変換を含め、低遅延で処理する必要があります。
毎秒数百万件のイベントを処理し、遅延データにも対応できる拡張性の高いソリューションを見つける必要があります。どうすればよいでしょうか?

A. データの取り込みと変換には Cloud Data Fusion を、ストレージと分析には Cloud SQL を使用します。
B. データの取り込みと保存にはFirebase Realtime Databaseを、処理と分析にはCloud Run関数を使用します。
C. データの取り込みには Pub/Sub を、処理には Apache Beam を使用した Dataflow を、ストレージと分析には BigQuery を使用します。
D. データの取り込みにはクラウドストレージ、バッチ処理にはApache Sparkを使用したDataproc、ストレージと分析にはBigQueryを使用します。


Question 5

分析用に10PBの製品履歴データを提供するアプリケーションのデータバックエンドを移行しました。製品の最新の状態(約10GBのデータ)のみをAPI経由で他のアプリケーションに提供する必要があります。分析要件と、1秒未満のレイテンシで最大1000クエリ/秒(QPS)のAPIパフォーマンスに対応できる、コスト効率の高い永続ストレージソリューションを選択する必要があります。どうすればよいでしょうか?

A. 1. 分析のために履歴データをクラウド SQLに保存します。
2. 別のテーブルに、製品が変更されるたびに、製品の最後の状態を保存します。
3. Cloud SQLからAPIに最新の状態データを直接提供する。
B. 1. 各製品に履歴変更のセットを持たせて、Firestore に製品をコレクションとして保存します。
2. 分析には、単純なクエリと複合クエリを使用する。
3. FirestoreからAPIに最新の状態データを直接提供する。
C. 1. 分析のために履歴データをBigQueryに保存します。
2. マテリアライズドビューを使用して、製品の最終状態を事前に計算します。
3. BigQueryからAPIに最新の状態データを直接提供する。
D. 1. 分析のために履歴データをBigQueryに保存します。
2. Cloud SQL テーブルに、製品が変更されるたびに、製品の最終状態を保存します。
3. Cloud SQLからAPIに最新の状態データを直接提供する。


Solutions:

Question 1
Answer: C
Question 2
Answer: C
Question 3
Answer: A
Question 4
Answer: C
Question 5
Answer: D

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