Databricks-Certified-Data-Engineer-Professional勉強方法、Databricks-Certified-Data-Engineer-Professional日本語認定対策

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>> Databricks-Certified-Data-Engineer-Professional勉強方法 <<

Databricks-Certified-Data-Engineer-Professional日本語認定対策、Databricks-Certified-Data-Engineer-Professional的中問題集

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q203-Q208):

質問 # 203
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Attach To" privileges on the required cluster
B. Cluster creation allowed. "Can Restart" privileges on the required cluster
C. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
D. "Can Restart" privileges on the required cluster
E. "Can Manage" privileges on the required cluster

正解：D

解説：
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

質問 # 204
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
C. spark.sql("VACUUM my_table RETAIN 15 HOURS")
D. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")

正解：D

解説：
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.

質問 # 205
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
B. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
C. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
D. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

正解：C

解説：
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog.
Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

質問 # 206
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
B. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
C. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
D. Delete the failed run, disable the cluster policy, and re-execute all tasks.

正解：A

解説：
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

質問 # 207
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. Run unit tests against non-production data that closely mirrors production
B. Define and unit test functions using Files in Repos
C. Define and import unit test functions from a separate Databricks notebook
D. Define units test and functions within the same notebook

正解：A

解説：
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.

質問 # 208
......

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Databricks-Certified-Data-Engineer-Professional勉強方法、Databricks-Certified-Data-Engineer-Professional日本語認定対策

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Databricks-Certified-Data-Engineer-Professional日本語認定対策、Databricks-Certified-Data-Engineer-Professional的中問題集

Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q203-Q208):

最新-信頼的なDatabricks-Certified-Data-Engineer-Professional勉強方法試験-試験の準備方法Databricks-Certified-Data-Engineer-Professional日本語認定対策

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