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Databricks Certified Data Engineer Associate Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

Answer: D

NEW QUESTION # 80
A data provider must share a live Delta table with an external partner who uses an open-source Spark environment outside Databricks, without copying the data.
Which feature should be used?

A. A Unity Catalog external location with a storage credential granted to the partner
B. A materialized view exported as Parquet and delivered by SFTP
C. A deep clone written to a partner-owned S3 bucket nightly
D. Delta Sharing with a recipient and share

Answer: D

Explanation:
Delta Sharing is an open protocol that lets a provider share live data from Unity Catalog with recipients on any platform that supports the protocol, with no data replication. Access is governed by shares and recipients, and the recipient always reads the current version of the data.

NEW QUESTION # 81
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("cloudFiles") .option("cloudFiles.format", "json") .load("/input/path")
C. df = spark.readStream.format("json").load("input/path")
D. df = spark.readStream.format("cloud"),option("json").load("/input/path")

Answer: B

NEW QUESTION # 82
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 schema(schema) with option ("maxFilesPerTrigger", 1)
D. Replace spark.read with spark.readStream
E. Replace format("delta") with format("stream")

Answer: D

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

NEW QUESTION # 83
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: A

Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution

NEW QUESTION # 84
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