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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models |
| Topic 2: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Topic 3: Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
| Topic 4: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Topic 5: Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Topic 6: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Topic 7: Data Governance | 7% | - Manage data assets and metadata - Enforce data policies and standards - Use Unity Catalog for governance |
| Topic 8: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Topic 9: Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Topic 10: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:
1. ある組織は、Webアプリケーションとモバイルアプリケーションから顧客データを処理しています。データには、氏名、メールアドレス、電話番号、位置情報履歴などが含まれます。データは、バッチファイル(SFTPから毎日)とストリーミングJSONイベント(Kafkaからリアルタイム)の両方で受信されます。
データ プライバシー ポリシーに準拠するには、次の要件を満たす必要があります。
- 電子メール、電話などの個人識別情報(PII)
番号および IP アドレスは保存前にマスクまたは匿名化する必要があります。
- バッチパイプラインとストリーミングパイプラインの両方で一貫したPIIを適用する必要がある
取り扱い。
- マスキング ロジックは監査可能かつ再現可能である必要があります。
- マスクされたデータは、ダウンストリーム分析に使用できる必要があります。
データ エンジニアは、バッチ モードとストリーミング モードの両方をサポートし、PII にデータ マスキングを適用し、監査の追跡可能性を維持する準拠データ パイプラインを Databricks 上でどのように設計すればよいでしょうか。
A) ノートブックを使用してバッチ データを読み込み、SQL ウェアハウスを使用してストリーミング データを取り込みます。Silver テーブルで Unity Catalog 列マスクを使用して、保存後にフィールドを編集します。
B) バッチおよびストリーミング取り込みに Lakeflow 宣言型パイプラインを使用し、PII マスキング関数を定義して、Delta Lake に書き込む前の Bronze 取り込み中に適用します。
C) 系統追跡のために PII をマスクせずに Bronze に保存し、レポートに使用される Gold テーブルにマスキング ロジックを適用できるようにします。
D) Lakeflow 宣言型パイプラインを使用してバッチ データとストリーミング データの両方を取り込み、読み取り時に Unity カタログ列マスクを介してマスキングを適用して、取り込み中にデータが変更されないようにします。
2. 複雑なワークロードを監視する場合、クエリプランを確認できることは、ワークロードの動作を理解する上で非常に重要です。クエリプランの可視化はどこで確認できますか？
A) Spart UIのジョブタブの下
B) クエリプロファイラのクエリソースの下
C) Spark UIのSQL/DataFrameタブの下
D) クエリプロファイラのステージタブ
3. エンドツーエンド テストの主な利点を説明している記述はどれですか。
A) テストスイートの自動化が容易になります
B) アプリケーションの構成要素内のエラーを正確に特定します。
C) アプリケーションの実際の使用状況を厳密にシミュレートします。
D) すべてのコードパスとブランチのテスト範囲を提供します。
4. データエンジニアは、顧客がレンタル自転車を利用中にどこに持ち込んでいるかを把握するためのデータ取り込みパイプラインを構築しています。エンジニアは、時間の経過とともに、自転車のセンサーから送信されるデータに緯度や経度などの重要な詳細情報が含まれていないことに気づきました。下流のアナリストは、クリーンなレコードと隔離されたレコードの両方を個別に処理する必要があります。
データ エンジニアはすでに次のコードを持っています。
インポートDLT
pyspark.sql.functionsからexprをインポートする
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
A) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
B) @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)
C) @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
D) @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
5. ジョブ実行履歴の保持に関して正しい記述はどれですか?
A) 90日間、またはカスタム実行構成を通じて実行IDが再利用されるまで保持されます。
B) ジョブ実行ログをエクスポートまたは削除するまで保持されます
C) 60日間保存され、その後ログはアーカイブされます
D) 60日間保持され、その間にノートブックの実行結果をHTMLにエクスポートできます。
E) 30日間保持され、その間にジョブ実行ログをDBFSまたはS3に配信できます。
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |
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