QUESTION: 29

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
Ingesting all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

Answer(s): E

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QUESTION: 30

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

return spark.readStream.table("bronze")
return spark.readStream.load("bronze")
return spark.read.option("readChangeFeed", "true").table ("bronze")

Answer(s): E

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QUESTION: 31

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.

The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Answer(s): D

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QUESTION: 32

The data engineering team maintains the following code:

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.

Answer(s): B

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QUESTION: 32