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Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Professional |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Exam Format: | Scenario-based questions, Multiple choice, Proctored online exam, Multiple select |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Real Exam Qty: | 45-60 |
| Passing Score: | 70% |
| Recommended Training: | Delta Lake Fundamentals Databricks Data Engineer Learning Path |
| Exam Registration: | Databricks Certification Portal Databricks Academy |
| Sample Questions: | ![]() |
| Exam Way: | Online proctored exam via Databricks certification platform |
| Pre Condition: | Recommended: Databricks Certified Data Engineer Associate or equivalent hands-on experience with Spark and Delta Lake |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Databricks-Certified-Data-Engineer-Professional日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Production Pipelines and Orchestration | - Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring |
| Data Modeling and Transformation | - Spark SQL transformations - Performance optimization techniques - Dimensional modeling concepts |
| Data Ingestion and Processing | - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns |
| Delta Lake and Data Management | - Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties |
| Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) |
Databricks-Certified-Data-Engineer-Professional日本語 Exam FAQ: What Candidates Ask About Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版)
The Databricks-Certified-Data-Engineer-Professional日本語 exam, officially known as Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版), is the Databricks test that leads to the Databricks Certified Data Engineer Professional certification at the Professional level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as Databricks Certified Data Engineer Associate.
The Databricks-Certified-Data-Engineer-Professional日本語 exam contains 45-60 questions, and you have 120 minutes to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.
The passing score for the Databricks-Certified-Data-Engineer-Professional日本語 exam is 70%, and the official registration fee is $200 USD. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.
Recommended: Databricks Certified Data Engineer Associate or equivalent hands-on experience with Spark and Delta Lake
Entry requirements can change, so confirm the latest conditions on the official exam page: https://www.databricks.com/learn/certification/data-engineer-professional.
You can book the Databricks-Certified-Data-Engineer-Professional日本語 exam through the official registration channels below:
Exam delivery: Online proctored exam via Databricks certification platform. Seats at popular test centers fill quickly, so schedule early once your preparation is on track.
Databricks recommends the following training options for Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) candidates:
Pair any course with the 250 practice questions from Actual4Exams to measure how ready you really are before paying the exam fee.
Yes. A free PDF demo of the Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.
If you take the corresponding Databricks-Certified-Data-Engineer-Professional日本語 exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.
The official Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) outline is organized into 5 domains. The first three are:
- Delta Lake and Data Management
- Data Modeling and Transformation
- Databricks Lakehouse Platform Architecture
See the complete exam topics section above for the full outline and the weighting of every domain.
Databricks Certified Data Engineer Professional Exam (Databricks-Certified-Data-Engineer-Professional日本語版) Sample Questions:
Question 1
Spark ドライバーの log4j 出力を解析するときに、テキストの重要な領域を識別するために使用できるテクノロジは次のどれですか。
A. pyspsark.ml.feature
B. Scala Datasets
C. C++
D. Regex
E. Julia
Question 2
データ エンジニアは、ストリーミング注文データを処理するための Lakeflow 宣言型パイプラインを設計しています。
パイプラインはオートローダーを使用してデータを取り込み、customer_idとamountが0より大きいことを確認することでデータ品質を強化する必要があります。無効なレコードは削除する必要があります。Lakeflowの宣言型パイプライン構成のうち、Pythonを使用してこの要件を実装できるものはどれですか?
A. @dlt.table
@dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
B. @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect("valid_customer", "customer_id IS NOT NULL")
.expect("valid_amount", "amount > 0")
)
C. @dlt.table
@dlt.expect("valid_customer", "customer_id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
D. @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
.expect_or_drop("valid_amount", "amount > 0")
)
Question 3
Lakeflow 宣言型パイプラインの運用面は、Spark Structured Streaming とどう違うのでしょうか?
A. Lakeflow 宣言型パイプラインはスキーマの進化を自動的に処理しますが、構造化ストリーミングでは常に手動のスキーマ管理が必要です。
B. Lakeflow 宣言型パイプラインは Delta Lake 形式に書き込むことができますが、構造化ストリーミングは書き込むことができません。
C. 構造化ストリーミングは継続的なデータ ストリームを処理できますが、Lakeflow 宣言型パイプラインは処理できません。
D. Lakeflow 宣言型パイプラインはマルチステージ パイプラインのオーケストレーションを自動的に管理しますが、構造化ストリーミングでは複雑な依存関係のために外部オーケストレーションが必要です。
Question 4
運用ワークロードは、外部の変更データキャプチャフィードからの更新を、常時稼働の構造化ストリームジョブとして Delta Lake テーブルに段階的に適用します。このテーブルのデータが最初に移行された際に、OPTIMIZE が実行され、ほとんどのデータファイルのサイズが 1 GB に変更されました。ストリーミング運用ジョブでは、自動最適化と自動圧縮の両方がオンになっていました。最近のデータファイルのレビューでは、テーブル内の各パーティションには少なくとも 1 GB のデータが含まれており、テーブル全体のサイズは 10 TB を超えているにもかかわらず、ほとんどのデータファイルは 64 MB 未満であることがわかりました。
ファイル サイズが小さくなる理由として考えられるのは次のうちどれでしょうか?
A. テーブルで計算されたZオーダーインデックスがファイルの圧縮を妨げています。C テーブルで計算されたブルームフィラーインデックスがファイルの圧縮を妨げています。
B. Databricks は、各パーティションのデータ量に基づいて、ターゲット ファイル サイズを小さくするように自動調整しました。
C. Databricks は、MERGE 操作の所要時間を短縮するために、ターゲット ファイル サイズを小さくするように自動調整しました。
D. Databricks は、テーブル内のデータの全体的なサイズに基づいて、ターゲット ファイルのサイズを小さくするように自動調整しました。
Question 5
データ取り込みタスクでは、1TBのJSONデータセットを、ターゲット部分ファイルサイズ512MBでParquetに書き出す必要があります。Delta LakeではなくParquetを使用しているため、自動最適化や自動圧縮などの組み込みファイルサイズ調整機能は使用できません。
データをシャッフルせずに最高のパフォーマンスを実現する戦略はどれですか?
A. spark.sql.files.maxPartitionBytes を 512 MB に設定し、データを取り込み、ナロー変換を実行してから、parquet に書き込みます。
B. spark.sql.shuffle.partitions を 2,048 パーティション (1TB*1024*1024/512) に設定し、データを取り込み、ナロー変換を実行し、データをソートして最適化し (これにより、データが自動的に再パーティション化されます)、parquet に書き込みます。
C. spark.sql.adaptive.advisoryPartitionSizeInBytes を 512 MB バイトに設定し、データを取り込み、ナロー変換を実行し、2,048 個のパーティション (1TB*1024*1024/512) に結合してから、parquet に書き込みます。
D. spark.sql.shuffle.partitions を 512 に設定し、データを取り込み、ナロー変換を実行してから、parquet に書き込みます。
E. データを取り込み、ナロー変換を実行し、2,048 個のパーティション (1TB*) に再分割します。
1024*1024/512)を計算し、parquet に書き込みます。
Solutions:
| Question 1 Answer: D | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: B |
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