Databricks Databricks Certification Databricks-Certified-Data-Engineer-Professional

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JapanCert試験問題集はPDF版とソフト版を提供します。PDF版は印刷されることができます、ソフト版はどのパソコンでも使われることもできます。

JapanCertの試験資料を買うかどうかと迷ったら、Databricks Databricks-Certified-Data-Engineer-Professional 「Databricks Certified Data Engineer Professional Exam」 試験の部分問題と回答を無料にダウンロードして試用する後、決めて信じてくれます。早ければJapanCertを信じてくれて、早く成功になっています。

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Databricks-Certified-Data-Engineer-Professionalオンライン版は Windows / Mac / Android / iOS 対応です。

Databricks Databricks-Certified-Data-Engineer-Professional 試験シラバストピック:

Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題：

問題 #1

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
C. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
D. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
E. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

問題 #2

A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Use a SQL CONSTRAINT block referencing the JSON file path.
B. Reference each expectation with @dlt.expect decorators in the table declaration.
C. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
D. Invoke an external API to validate records against the metadata rules.

問題 #3

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

問題 #4

Which Python variable contains a list of directories to be searched when trying to locate required modules?

A. os.path
B. pypi.path
C. importlib.resource path
D. sys.path
E. pylib.source

問題 #5

A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

解説：