Valid Databricks-Certified-Data-Engineer-Professional Exam Syllabus, Detailed Databricks-Certified-Data-Engineer-Professional Study Plan

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
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 and ltv columns will be returned with the values in user itv.
B. Three columns will be returned, but one column will be named "redacted" and contain only null values.
C. The email, age. and ltv columns will be returned with the values in user ltv.
D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E. Only the email and itv columns will be returned; the email column will contain all null values.

Answer: D

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.

NEW QUESTION # 124
In order to facilitate near real-time workloads, a data engineer is creating a helper function to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A.
B.
C.
D. Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
E.

Answer: E

Explanation:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html

NEW QUESTION # 125
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
B. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
E. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Answer: E

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 126
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
C. When a database is being created, make sure that the LOCATION keyword is used.
D. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
E. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

Answer: D

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

NEW QUESTION # 127
Which of the following is true of Delta Lake and the Lakehouse?

A. Z-order can only be applied to numeric values stored in Delta Lake tables
B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
E. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Answer: C

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 128
......

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