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NEW QUESTION # 113
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
Answer: A
Explanation:
Comprehensive and Detailed
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).
Why not the others?
B: Uses @dlt.expect_or_drop, which drops invalid records instead of quarantining them. This violates the requirement that quarantined data should be available.
C: Same as B, but applies expectations in bulk with expect_all_or_drop. Again, bad data is dropped, not quarantined.
D: Adds an is_quarantined flag in the same table. While it marks bad records, it does not separate them into a distinct quarantine table as required by the business use case.
Therefore, Option A is the only solution aligned with Databricks documentation for quarantining invalid data into a dedicated table while keeping valid data in the main pipeline.
NEW QUESTION # 114
What is the purpose of a gold layer in Multi-hop architecture?
Answer: E
Explanation:
Explanation
The answer is Powers ML applications, reporting, dashboards and adhoc reports.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1.Powers Ml applications, reporting, dashboards, ad hoc analytics
2.Refined views of data, typically with aggregations
3.Reduces strain on production systems
4.Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
NEW QUESTION # 115
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
Which statement describes this implementation?
Answer: C
Explanation:
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.
NEW QUESTION # 116
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?
Answer: A
Explanation:
* 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." References: Structured Streaming watermarking and deduplication; One-time trigger.
NEW QUESTION # 117
A platform team lead is responsible for automating SQL Warehouse usage attribution across business units.
They need to identify warehouse usage at the individual user level and share a daily usage report with an executive team that includes business leaders from multiple departments.
How should the platform lead generate an automated report that can be shared daily?
Answer: C
Explanation:
Databricks provides system tables under the system.billing and system.access schemas for auditing and cost attribution across SQL Warehouses. The best practice is to build a dashboard in Databricks SQL using these system tables and configure it with a daily refresh schedule . This ensures automated, up-to-date reporting of compute consumption, user activity, and cost distribution. Sharing this dashboard with executives provides transparency without requiring them to run queries manually. This approach aligns with Databricks' operational guidance on workspace observability and cost governance , making B the correct answer.
NEW QUESTION # 118
......
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