Databricks-Certified-Professional-Data-Engineer無料問題集「Databricks Certified Professional Data Engineer」

質問 1

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

（A）No permissions

（B）Can Read

（C）Can Manage

（D）Can Edit

（E）Can Run

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

（A）The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

（B）The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

（C）The total average temperature across all sensors exceeded 120 on three consecutive executions of the query

（D）The source query failed to update properly for three consecutive minutes and then restarted

（E）The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

（A）Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

（B）Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

（C）Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires

（D）Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

（A）The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

（B）Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

（C）No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

（D）The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

（E）Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

（A）All records are cached to attached storage and then the filter is applied

（B）The Delta log is scanned for min and max statistics for the latitude column

（C）The Hive metastore is scanned for min and max statistics for the latitude column

（D）All records are cached to an operational database and then the filter is applied

（E）The Parquet file footers are scanned for min and max statistics for the latitude column

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

（A）Define units test and functions within the same notebook

（B）Run unit tests against non-production data that closely mirrors production

（C）Define and unit test functions using Files in Repos

（D）Define and import unit test functions from a separate Databricks notebook

正解：B 解答を投票する

質問 7

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

（A）delayWrite("event_time", "10 minutes")

（B）withWatermark("event_time", "10 minutes")

（C）await("event_time + '10 minutes'")

（D）slidingWindow("event_time", "10 minutes")

（E）awaitArrival("event_time", "10 minutes")

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

（A）Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git

（B）Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

（C）Stop the existing pipeline; use the returned settings in a reset command

（D）Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

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）Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.

（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 databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.

（D）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.

（E）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.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

（A）In the Delta Lake transaction log. by noting the column statistics

（B）In the Query Detail screen, by interpreting the Physical Plan

（C）In the Executor's log file, by gripping for "predicate push-down"

（D）In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

（E）In the Storage Detail screen, by noting which RDDs are not stored on disk

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Databricks-Certified-Professional-Data-Engineer 無料問題集「Databricks Certified Professional Data Engineer」

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