ハイパスレートのDatabricks-Certified-Professional-Data-Engineer受験料過去問一回合格-100％合格率のDatabricks-Certified-Professional-Data-Engineer資格関連題

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DataBricks認定プロフェッショナルデータエンジニア認定は、DataBricksプラットフォームの使用に関する専門知識を実証したいデータエンジニアにとって貴重な資格です。雇用主に候補者と従業員のスキルを特定して検証する方法を提供します。また、データエンジンがDataBricksプラットフォームを使用してスケーラブルで信頼できるデータパイプラインを構築および維持する習熟度を実証することにより、キャリアを進めるのに役立ちます。

試験の準備をするために、DataBricksは、オンラインコース、ワークショップ、認定ブートキャンプなど、さまざまなトレーニングリソースを提供しています。これらのリソースは、DataBricksプラットフォーム上のデータエンジニアリング、データサイエンス、機械学習、データ分析などのトピックをカバーしています。さらに、候補者はDataBricks Academyにアクセスすることもできます。これは、自己ペースの学習モジュールと実践試験を提供して、認定試験の準備を支援することもできます。

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Databricks認定プロフェッショナルデータエンジニア試験に合格することは、どのデータエンジニアにとっても大きな成果です。候補者は、Databricksの操作において高いレベルの専門知識を持ち、複雑なデータパイプラインを設計および管理できることを示しています。認定は雇用主によって高く評価されており、新しいキャリアの機会とより高い給与につながる可能性があります。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q98-Q103):

質問 # 98
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data.
Which configuration will provide the optimal performance while enabling these capabilities?

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams. Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support. Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.


質問 # 99
Which of the following Auto loader structured streaming commands successfully performs a hop from the landing area into Bronze?

正解：C

解説：
Explanation
The answer is
1.spark\
2..readStream\
3..format("cloudFiles") \# use Auto loader
4..option("cloudFiles.format","csv") \ # csv format files
5..option("cloudFiles.schemaLocation", checkpoint_directory)\
6..load('landing')\
7..writeStream.option("checkpointLocation", checkpoint_directory)\
8..table(raw)
Note: if you chose the below option which is incorrect because it does not have readStream
1.spark.read.format("cloudFiles")
2..option("cloudFiles.format","csv")
3....
4...
5...
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence


質問 # 100
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

正解：A

解説：
* Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible."
* Exact extract: "Trigger once processes all available data once and then stops." References: Structured Streaming triggers; Databricks Jobs and job clusters.


質問 # 101
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?

正解：C

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 References:
[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 # 102
A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement - the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

正解：A

解説：
Comprehensive and Detailed Explanation from Databricks Documentation:
Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect, @dlt.expect_or_drop, and @dlt.expect_all.
@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows. Instead, failing rows can be written to a quarantine table.
@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.
In this case, the requirement is:
Good rows (reading < 120) go to the silver table.
Bad rows (reading >= 120) go to a quarantine table.
Bad rows should not be included in silver.
The correct implementation is Option A, where:
The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.
The quarantine table applies an expectation for reading >= 120, ensuring bad records are captured separately.
Other options are incorrect:
Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.
Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.
Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.


質問 # 103
......

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