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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題:

1. A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A) Merge, as the job configuration looks good.
B) Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
C) Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
D) Reject, as they should use {{job.trigger_time.is_weekday}} instead.

2. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
C) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
D) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

3. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
B) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
C) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
D) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
E) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.

4. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Force a sort-merge join with /*+ MERGE(r) */.
B) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
C) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
D) Increase spark.sql.shuffle.partitions from 200 to 10000.

5. A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

A) Implement a write operation using MERGE INTO with a unique key
B) Use Delta Lake's change data feed to filter duplicate records
C) Use Delta Lake time travel to identify and remove duplicates
D) Configure Structured Streaming with dropDuplicates transformation

問題與答案：