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

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Databricks Certified Data Engineer Professional試験学習資料の三つバージョン

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

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

問題 #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 stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
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. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

問題 #2

How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
B. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
C. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
D. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.

問題 #3

Which statement describes the default execution mode for Databricks Auto Loader?

A. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
B. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
D. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
E. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

問題 #4

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. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
B. 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.
C. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D. 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.
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 #5

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. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
E. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

解説：