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Databricks Certified Data Engineer Associate Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Answer: C

Explanation:
Explanation
The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. -- in the link search for "The engine uses " youll find the answer.https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#:~:text=The%20engin


NEW QUESTION # 59
A Python file is ready to go into production and the client wants to use the cheapest but most efficient type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations. Which cluster meets the requirement?

Answer: D

Explanation:
A job cluster with spot instances enabled is the cheapest and efficient option for small, production workloads. Spot instances reduce costs, and a job cluster ensures the cluster only runs during the workload execution.


NEW QUESTION # 60
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?

Answer: A

Explanation:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly.
External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
=========


NEW QUESTION # 61
A data engineer is loading a dataset into a Delta table but expects the schema to evolve over time as new columns are added. The pipeline should automatically handle new fields without failing ingestion jobs. Which Delta Lake option should the engineer enable during the write operation?

Answer: A


NEW QUESTION # 62
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean.
However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

Answer: C


NEW QUESTION # 63
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