Databricks-Certified-Professional-Data-Engineer試験無料問題集（122題）「Databricks Certified Professional Data Engineer 認定」

出題：1

Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.

B. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

C. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.

D. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

E. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.

B. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

C. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

D. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

E. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. Data will be marked as deleted but still recoverable with Time Travel.

B. An error will occur because Delta Lake prevents the deletion of production data.

C. The table will be removed from the catalog but the data will remain in storage.

D. Nothing will occur until a COMMIT command is executed.

E. The table will be removed from the catalog and the data will be deleted.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When tables are created, make sure that the external keyword is used in the create table statement.

B. Whenever a database is being created, make sure that the location keyword is used

C. When the workspace is being configured, make sure that external cloud object storage has been mounted.

D. Whenever a table is being created, make sure that the location keyword is used.

E. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the skipChangeCommits flag to true on bpm_stats

B. Set the SkipChangeCommits flag to true raw_lot

C. Set the pipelines, reset, allowed property to false on bpm_stats

D. Set the pipelines, reset, allowed property to false on raw_iot

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

What is the first of a Databricks Python notebook when viewed in a text editor?

A. %python

B. //Databricks notebook source

C. % Databricks notebook source

D. -- Databricks notebook source

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

C. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

D. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.

E. Modify the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

A.

B.

C.

D.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

B. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

D. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.
A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

B. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

C. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

D. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

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