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

1. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
B) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
C) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
D) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
E) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.

2. The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

3. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
C) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
E) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.

4. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
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The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A) preds.write.format("delta").save("/preds/churn_preds")
B)

C) preds.write.mode("append").saveAsTable("churn_preds")
D)

E)

5. A table named user_ltv is being used to create a view that will be used by data analysts on Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A) The email, age. and ltv columns will be returned with the values in user ltv.
B) Three columns will be returned, but one column will be named "redacted" and contain only null values.
C) Only the email and itv columns will be returned; the email column will contain all null values.
D) Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E) The email and ltv columns will be returned with the values in user itv.

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