Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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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：

1. Which statement describes a key benefit of an end-to-end test?

A) It provides testing coverage for all code paths and branches.
B) It pinpoint errors in the building blocks of your application.
C) It makes it easier to automate your test suite
D) It closely simulates real world usage of your application.

2. A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A) Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
B) Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
C) Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
D) Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.

3. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
B) The update throws an error because changes to existing columns in the target schema are not supported.
C) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

4. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A) Network I/O never spikes
B) The five Minute Load Average remains consistent/flat
C) Total Disk Space remains constant
D) Bytes Received never exceeds 80 million bytes per second
E) Overall cluster CPU utilization is around 25%

5. 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) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
B) 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.
C) 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.
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) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

質問と回答：