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

Section	Objectives
Topic 1: Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Topic 2: Data Modeling and Storage	- Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization
Topic 3: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 4: Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
In order to facilitate near real-time workloads, a data engineer is creating a helper function to 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. Option E
B. Option B
C. Option D
D. Option A
E. Option C

Answer: B

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the
"cloudFiles.schemaLocation" option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
* Write streaming data:
https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

NEW QUESTION # 151
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the fieldpk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
B. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
C. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
D. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
E. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.

Answer: D

Explanation:
Explanation
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will alwaysreflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 152
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 ofVACUUMcommands 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 vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
B. 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.
C. 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.
D. 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.
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.

Answer: E

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

NEW QUESTION # 153
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format("parquet")
.load("/mnt/raw_orders/")
.withWatermark("time", "2 hours")
.dropDuplicates(["customer_id", "order_id"])
.writeStream
.trigger(once=True)
.table("orders")
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
B. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
D. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay." Exact extract: "Records older than the event-time watermark are considered late and may be dropped." Exact extract: "trigger(once) processes all available data once and then stops."

NEW QUESTION # 154
Which of the statement is correct about the cluster pools?

A. Cluster pools are used to share resources among multiple teams
B. Cluster pools allow you to create a cluster
C. Cluster pools allow you to have all the nodes in the cluster from single physical server rack
D. Cluster pools allow you to save time when starting a new cluster
E. Cluster pools allow you to perform load balancing

Answer: D

NEW QUESTION # 155
......

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