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Databricks Databricks-Certified-Professional-Data-Engineer関連受験参考書 & Databricks-Certified-Professional-Data-Engineer試験問題

私たちDatabricksは非常に人気があり、詳細で完璧なGoShiken顧客サービスシステムを持っています。 まず、Databricks-Certified-Professional-Data-Engineerの実際の試験の顧客によるオンライン支払いが成功してから5〜10分後に、顧客サービスから電子メールを受信し、すぐにDatabricks Certified Professional Data Engineer Exam学習を開始できます。 また、Databricks-Certified-Professional-Data-Engineer試験問題を毎日確認および更新する専任スタッフがいるため、Databricks-Certified-Professional-Data-Engineer試験教材の最新情報を購入するたびに入手できます。 第二に、24時間体制のサービスをお客様に提供します。 Databricks-Certified-Professional-Data-Engineer学習教材に関する問題は、いつでもどこでも必要に応じて解決できます。

Databricks Certified Professional Data Engineer試験は、Databricksを使用して一連のタスクを完了することが求められる実践的な試験です。この試験は、データパイプラインを設計・実装し、データソースやシンクを操作し、Databricksを使用して変換を行う能力を評価します。また、データパイプラインをパフォーマンスや信頼性の観点から最適化し、チューニングする能力も試験されます。

>> Databricks Databricks-Certified-Professional-Data-Engineer関連受験参考書 <<

試験Databricks Databricks-Certified-Professional-Data-Engineer関連受験参考書 & 実際的なDatabricks-Certified-Professional-Data-Engineer試験問題 | 大人気Databricks-Certified-Professional-Data-Engineer模擬試験サンプル

Databricks-Certified-Professional-Data-Engineer認定試験は試験に関連する書物を学ぶだけで合格できるものではないです。がむしゃらに試験に要求された関連知識を積み込むより、価値がある問題を勉強したほうがいいです。効率のあがる試験問題集は受験生の皆さんにとって欠くことができないツールです。ですから、はやくGoShikenのDatabricks-Certified-Professional-Data-Engineer問題集を入手しましょう。これは高い的中率を持っている問題集で、ほかのどのような勉強法よりもずっと効果があるのです。これはあなたが一回で楽に成功できるを保証するめぼしい参考書です。

Databricks Databricks-Certified-Professional-Data-Engineer 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.

トピック 2

• Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.

トピック 3

• Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.

 

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q126-Q131):

質問 # 126
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

• A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
• B. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
• C. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
• D. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
• E. A managed table will be created in the DBFS root storage container.

正解：D

解説：
https://docs.databricks.com/en/lakehouse/data-objects.html

 

質問 # 127
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?

• A. A data lakehouse provides a relational system of data management
• B. A data lakehouse utilizes proprietary storage formats for data
• C. A data lakehouse captures snapshots of data for version control purposes
• D. A data lakehouse couples storage and compute for complete control
• E. A data lakehouse enables both batch and streaming analytics

正解：E

 

質問 # 128
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

• A. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
• B. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
• D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

正解：A

解説：
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

 

質問 # 129
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

• A. USING CSV
  OPTIONS ( header ="true", delimiter = "|")
  (Correct)
• B. USING CSV
  TYPE ( "true","|")
• C. FORMAT CSV
  OPTIONS ( "true","|")
• D. FORMAT CSV
  FORMAT TYPE ( header ="true", delimiter = "|")
• E. FORMAT CSV
  TYPE ( header ="true", delimiter = "|")

正解：A

解説：
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"

 

質問 # 130
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

• A. All related files and metadata are dropped and recreated in a single ACID transaction.
• B. The table reference in the metastore is updated and no data is changed.
• C. The table reference in the metastore is updated and all data files are moved.
• D. The table name change is recorded in the Delta transaction log.
• E. A new Delta transaction log Is created for the renamed table.

正解：B

解説：
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

 

質問 # 131
......

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