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QUESTION 10
The view updates represents an incremental batch of all newly ingested data to be inserted or Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

QUESTION 11
Which statement describes the default execution mode for Databricks Auto Loader?

New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.

New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.

QUESTION 12
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from

When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

Whenever a table is being created, make sure that the location keyword is used.

When tables are created, make sure that the external keyword is used in the create table statement.

When the workspace is being configured, make sure that external cloud object storage has been mounted.

QUESTION 13
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

Delta Lake statistics are not optimized for free text fields with high cardinality.

Text data cannot be stored with Delta Lake.

ZORDER ON review will need to be run to see performance gains.

The Delta log creates a term matrix for free text fields to support selective filtering.

Delta Lake statistics are only collected on the first 4 columns in a table.

QUESTION 14
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

configure

fs

jobs

libraries

workspace

QUESTION 15
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.

All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.

All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.

QUESTION 16
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
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Which command should be removed from the notebook before scheduling it as a job?

Cmd 2

Cmd 3

Cmd 4

Cmd 5

Cmd 6

QUESTION 17
The data engineering team maintains the following code:
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Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.

An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

QUESTION 18
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

Can Manage

Can Edit

No permissions

Can Read

Can Run

QUESTION 19
A data architect has heard about lake’s built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from information is critical to this decision?

Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.

QUESTION 20
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format(“parquet”).load(f”/mnt/source/(date)”)
Which code block should be used to create the date Python variable used in the above code block?

date = spark.conf.get(“date”)

input_dict = input()
date= input_dict[“date”]

import sys
date = sys.argv[1]

date = dbutils.notebooks.getParam(“date”)

dbutils.widgets.text(“date”, “null”)
date = dbutils.widgets.get(“date”)

QUESTION 21
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
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Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

QUESTION 22
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:
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Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.

Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

QUESTION 23
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

Use &Pip install in a notebook cell

Run source env/bin/activate in a notebook setup script

Install libraries from PyPi using the cluster UI

Use &sh install in a notebook cell

QUESTION 24
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the “Owner” for each job.
They attempt to transfer “Owner” privileges to the “DevOps” group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from

Databricks jobs must have exactly one owner; “Owner” privileges cannot be assigned to a group.

The creator of a Databricks job will always have “Owner” privileges; this configuration cannot be changed.

Other than the default “admins” group, only individual users can be granted privileges on jobs.

A user can only transfer job ownership to a group if they are also a member of that group.

Only workspace administrators can grant “Owner” privileges to a group.

QUESTION 25
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake’s upsert functionality.

Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

QUESTION 26
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

When tables are created, make sure that the external keyword is used in the create table statement.

When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam’s Question and Answers from

When the workspace is being configured, make sure that external cloud object storage has been mounted.

Whenever a table is being created, make sure that the location keyword is used.

QUESTION 27
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

QUESTION 28
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?

Three new jobs named “Ingest new data” will be defined in the workspace, and they will each run once daily.

The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Three new jobs named “Ingest new data” will be defined in the workspace, but no jobs will be executed.

One new job named “Ingest new data” will be defined in the workspace, but it will not be executed.

The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

QUESTION 29
Which statement describes a key benefit of an end-to-end test?

It closely simulates real world usage of your application.

It pinpoint errors in the building blocks of your application.

It provides testing coverage for all code paths and branches.

It makes it easier to automate your test suite

QUESTION 30
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

In the Executor’s log file, by gripping for “predicate push-down”

In the Stage’s Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

In the Storage Detail screen, by noting which RDDs are not stored on disk

In the Delta Lake transaction log. by noting the column statistics

In the Query Detail screen, by interpreting the Physical Plan

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