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Databricks Certified Data Engineer Associate Exam Sample Questions (Q234-Q239):

NEW QUESTION # 234
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: D

NEW QUESTION # 235
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format
B. A job that ingests raw data from a streaming source into the Lakehouse
C. A job that queries aggregated data designed to feed into a dashboard
D. A job that aggregates uncleaned data to create standard summary statistics
E. A job that cleans data by removing malformatted records

Answer: C

Explanation:
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. Reference: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.

NEW QUESTION # 236
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger()
B. trigger(processingTime="5 seconds")
C. trigger("5 seconds")
D. trigger(once="5 seconds")
E. trigger(continuous="5 seconds")

Answer: B

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 237
What is the primary function of the Silver layer in the Databricks medallion architecture?

A. Aggregate and enrich data for business analytics
B. Store historical data solely for auditing purposes
C. lngest raw data in its original state
D. Validate, clean, and deduplicate data for further processing

Answer: D

NEW QUESTION # 238
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace predict with a stream-friendly prediction function
B. Replace "transactions" with the path to the location of the Delta table
C. Replace spark.read with spark.readStream
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace format("delta") with format("stream")

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html

NEW QUESTION # 239
......

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