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Databricks Certified Data Engineer Associate Exam Sample Questions (Q165-Q170):

NEW QUESTION # 165
A data engineer is designing a Gold-layer table for a BI team that needs to query complex aggregations. The data should be precomputed to provide low latency for dashboard users, while the underlying source data changes only once every few hours.
Which object type should be implemented in Unity Catalog?

A. Streaming table
B. View
C. External table
D. Materialized view

Answer: D

NEW QUESTION # 166
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several json files from containers on a storage account as soon as the file arrives within the storage account.
Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.read.json( " inp i./path " )
B. df = spark.readStream.format( " json " ).load( " input/path " )
C. df = spark.readStream.format( " cloud " ),option( " json " ).load( " /input/path " )
D. df = spark.readStream.format( " cloudFiles " ) .option( " cloudFiles.format " , " json " ) .load( " /input
/path " )

Answer: D

NEW QUESTION # 167
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:

Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query

A. Cache the dataset in order to boost the query performance
B. Upsize the driver node and deactivate autoshuffle partitions
C. Upsize the worker nodes and activate autoshuffle partitions
D. Fix the shuffle partitions to 50 to ensure the allocation

Answer: B,C

NEW QUESTION # 168
Which of the following data workloads will utilize a Gold table as its source?

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

Answer: A

Explanation:
1: 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 # 169
Which Databricks asset bundle format is valid?

A. resources: jobs: hello-job: name: hello-job tasks: - task_key: hello-task existing_cluster_id: 1234-
567890-abcde123 notebook_task: notebook_path: ./hello.py
B. resources { jobs { name = "hello-job" tasks{ task_key = "hello-task" existing_cluster_id = "1234-
567890-abcde123" notebook_task{ notebook_path = ".hello.py" } } } }
C. "resources":{ "jobs":{ "name":"hello-job", "tasks":{ "task_key:"hello-task",
"existing_cluster_id":"1234-567890-abcde123", "notebook_task":{ "notebook_path":
".hello.py" } } }
D. configuration = { "resources":{ "jobs":{ "name":"hello-job", "tasks":{ "task_key:"hello-task",
"existing_cluster_id":"1234-567890-abcde123", "notebook_task":{ "notebook_path":
".hello.py" } } } }

Answer: A

Explanation:
Databricks asset bundles use YAML format, where resources are defined hierarchically with indentation. Option A correctly follows the YAML structure for defining a job, task key, cluster, and notebook path.

NEW QUESTION # 170
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