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

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
C) 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.
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) 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.

2. A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A) Partition the Delta table by account_id
B) Enable automatic file compaction on writes
C) Enable deletion vectors on the Delta table
D) Enable change data feed on the Delta table

3. An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A) A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B) An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.
C) A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
D) A managed table will be created in the DBFS root storage container.
E) An external table will be created in the storage container mounted to /mnt/finance eda bucket.

4. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A) Parse the Delta Lake transaction log to identify all newly written data files.
B) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
C) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
D) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

5. What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A) Use &Pip install in a notebook cell
B) Install libraries from PyPi using the cluster UI
C) Run source env/bin/activate in a notebook setup script
D) Use &sh install in a notebook cell

Solutions: