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To prepare for the exam, Databricks offers a range of training resources, including online courses, workshops, and certification bootcamps. These resources cover topics such as data engineering, data science, machine learning, and data analytics on the Databricks platform. Additionally, candidates can also access the Databricks Academy, which provides self-paced learning modules and practice exams to help them prepare for the certification exam.
Databricks Certified Professional Data Engineer Exam is an excellent way for data engineers to demonstrate their expertise in working with the Databricks platform. It provides employers with a way to validate the skills of potential candidates and ensures that data engineers have the knowledge and skills needed to work with Databricks effectively.
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Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their skills and proficiency in using Databricks for data engineering tasks. Databricks Certified Professional Data Engineer Exam certification exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks Certified Professional Data Engineer Exam certification can help data engineers to advance their careers and increase their earning potential, and it can also help organizations to identify and hire skilled data engineers who can design and implement data solutions using Databricks.
NEW QUESTION # 111
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?
Answer: D
NEW QUESTION # 112
A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?
Answer: B
Explanation:
Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.
All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.
Reference Source: Databricks Compute and Job Cluster Configuration Documentation - "Autoscaling and Job Clusters."
NEW QUESTION # 113
A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.
Which sequence of API actions should the data engineer perform?
Answer: C
Explanation:
The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:
* GET /api/2.1/jobs/list - Lists all available jobs within the workspace.
* GET /api/2.1/jobs/runs/list - Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).
* POST /api/2.1/jobs/run-now - Immediately triggers a rerun of the specified job.
This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.
NEW QUESTION # 114
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?
Answer: D
Explanation:
most recent value for each username.
Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.
NEW QUESTION # 115
A data engineering team is building an append-only data pipeline with Delta Lake and wants to ensure data is never changed or deleted after it has been written.
Which Delta Lake feature should the data engineer enable to prevent changes to existing data?
Answer: D
Explanation:
Delta Lake provides the delta.appendOnly table property to restrict modification operations on an existing table.
When this property is configured as true, the table accepts append operations while disallowing updates and deletions of existing records.
This behavior is useful for immutable event logs, audit records, historical ingestion datasets, and other workloads requiring append-only storage semantics.
The property can be enabled using ALTER TABLE with SET TBLPROPERTIES, specifying delta.appendOnly = true.
Option B is incorrect because VACUUM removes eligible obsolete data files according to retention policies. Option C is incorrect because OPTIMIZE reorganizes data files for improved query efficiency. Option D is incorrect because Time Travel provides access to historical table versions rather than preventing modifications.
Therefore, the append-only table property directly enforces the required Delta Lake write restriction.
Databricks on Google Cloud
NEW QUESTION # 116
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