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更新されたDatabricks-Certified-Data-Engineer-Professional試験参考書を得ることができ、取得方法？

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Tech4Examはどんな試験参考書を提供していますか？

テストエンジン：Databricks-Certified-Data-Engineer-Professional試験試験エンジンは、あなた自身のデバイスにダウンロードして運行できます。インタラクティブでシミュレートされた環境でテストを行います。
PDF（テストエンジンのコピー）：内容はテストエンジンと同じで、印刷をサポートしています。

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あなたのPCにダウンロードしてインストールすると、Databricks Databricks-Certified-Data-Engineer-Professionalテスト問題を練習し、'練習試験'と '仮想試験'2つの異なるオプションを使用してあなたの質問と回答を確認することができます。
仮想試験 - 時間制限付きに試験問題で自分自身をテストします。
練習試験 - 試験問題を1つ1つレビューし、正解をビューします。

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オンラインテストエンジンは、WEBブラウザをベースとしたソフトウェアなので、Windows / Mac / Android / iOSなどをサポートできます。どんな電設備でも使用でき、自己ペースで練習できます。オンラインテストエンジンはオフラインの練習をサポートしていますが、前提条件は初めてインターネットで実行することです。
ソフトテストエンジンは、Java環境で運行するWindowsシステムに適用して、複数のコンピュータにインストールすることができます。
PDF版は、Adobe ReaderやFoxit Reader、Google Docsなどの読書ツールに読むことができます。

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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A) Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B) Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
C) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
E) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

2. Which statement describes the default execution mode for Databricks Auto Loader?

A) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
B) Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
C) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
D) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
E) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

3. The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

A) Delta Lake statistics are not optimized for free text fields with high cardinality.
B) ZORDER ON review will need to be run to see performance gains.
C) Text data cannot be stored with Delta Lake.
D) The Delta log creates a term matrix for free text fields to support selective filtering.
E) Delta Lake statistics are only collected on the first 4 columns in a table.

4. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
B) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
C) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
D) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
E) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

5. The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A) Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
B) Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
C) Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
D) Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
E) Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

質問と回答：