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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
| Topic 2: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs |
| Topic 3: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 4: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Topic 5: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs |
| Topic 6: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 8: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance |
| Topic 9: Monitoring and Alerting | 10% | - Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health |
| Topic 10: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
Lakehouse内のcustomer_churn_paramsというテーブルは、機械学習チームによる顧客離脱予測に使用されています。このテーブルには、複数の上流ソースから得られた顧客情報が含まれています。現在、データエンジニアリングチームは、上流データソースから得られた最新の有効な値でこのテーブルを毎晩上書きすることで、データを更新しています。
MLチームが使用しているチャーン予測モデルは、本番環境では比較的安定しています。チームは過去24時間以内に変更されたレコードのみに基づいて予測を行うことに注力しています。
変更されたレコードの識別を簡素化するアプローチはどれでしょうか?
Correct Answer: E 🗳️
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データサイエンスチームは、MLflowを使用して本番環境モデルを作成し、ログに記録しました。このモデルは列名のリストを受け取り、DOUBLE型の新しい列を返します。
次のコードは、本番モデルを正しくインポートし、customer_id キー列を含む顧客テーブルを DataFrame に読み込み、モデルに必要な特徴列を定義します。
どのコード ブロックが、スキーマ「customer_id LONG、predictions DOUBLE」を持つ DataFrame を出力しますか。
Correct Answer: E 🗳️
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Spark へのデータの取り込み時に Spark パーティションのサイズに直接影響する構成パラメータはどれですか?
Correct Answer: C 🗳️
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データエンジニアが日次レポートジョブを作成しています。レポートノートブックは2つあり、1つは平日用、もう1つは週末用です。「if/else条件」タスクは次のように設定されています。
{{job.start_time.is_weekday}} == true と設定すると、ジョブは平日または週末のノートブックタスクにルーティングされます。同じジョブが複数のタイムゾーンで使用されます。シニアデータエンジニアは、ジョブをレビューした後、プルリクエストをマージするか拒否するかについて、どのようなアクションを取るべきでしょうか?
Correct Answer: B 🗳️
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データエンジニアは、Databricks 間のシナリオにおいて読み取りパフォーマンスを最適化するために、Delta Sharing を設定しようとしています。受信者は、共有された売上データに対してタイムトラベルクエリとストリーミング読み取りを実行する必要があります。これらの機能を有効にしながら最適なパフォーマンスを実現するには、どの構成が最適でしょうか?
Correct Answer: C 🗳️
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