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[General] Authentic Databricks Databricks-Certified-Professional-Data-Engineer Exam Questi

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【General】 Authentic Databricks Databricks-Certified-Professional-Data-Engineer Exam Questi

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q96-Q101):NEW QUESTION # 96
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?
  • A. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
  • B. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
  • C. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
  • D. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
  • E. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
Answer: A
Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

NEW QUESTION # 97
A junior data engineer on your team has implemented the following code block.

The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?
  • A. They are ignored.
  • B. They are deleted.
  • C. They are merged.
  • D. They are inserted.
  • E. They are updated.
Answer: A
Explanation:
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged."https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20o

NEW QUESTION # 98
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
  • A. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
  • B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
  • C. Both commands will fail. No new variables, tables, or views will be created.
  • D. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
  • E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
Answer: B
Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

NEW QUESTION # 99
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
  • A. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
  • B. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
  • C. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
  • D. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
  • E. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
Answer: B
Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.

NEW QUESTION # 100
A table named user_ltv is being used to create a view that will be used by data analysis on 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:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?
  • A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
  • B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
  • C. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
  • D. All records from all columns will be displayed with the values in user_ltv.
Answer: A
Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 101
......
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