Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question - Page 4

Question 10 (Topic 1)

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A. If task A fails during a scheduled run, which statement describes the results of this run?

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○ Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
○ Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
○ Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
○ Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
○ Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

Reference / correct answer:

Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Most accepted answer: D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

Community votes: D=4

Selected Answer: D D is correct, taks B and C will definitely be skipped, since Task A is notebook, the ACID logic is at cell level, some logic might be executed before failing cell upvoted 6 times

Selected Answer: D Suretly D upvoted 1 times

Selected Answer: D In Databricks job execution, if a task that other tasks depend on fails, the dependent tasks will not be executed. Since Tasks B and C depend on the successful completion of Task A, they will be skipped if Task A fails. However, if Task A performs any operations that commit changes before the failure occurs (such as writing to a Delta table), those changes remain and are not automatically rolled back unless the logic within Task A specifically includes rollback mechanisms for partial failures. upvoted 4 times

D is the answer upvoted 3 times

Selected Answer: D Some ops in task A may have fished before fail upvoted 3 times

Question 11 (Topic 1)

Which statement regarding Spark configuration on the Databricks platform is true?

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○ The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs currently running on the cluster.
○ Spark configurations set within a notebook will affect all SparkSessions attached to the same interactive cluster.
○ Spark configuration properties can only be set for an interactive cluster by creating a global init script.
○ Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
○ When the same Spark configuration property is set for an interactive cluster and a notebook attached to that cluster, the notebook setting will always be ignored.

Reference / correct answer:

Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

Most accepted answer: D. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

Community votes: A=1, B=1, D=6

Selected Answer: D I tried it myself, setting a spark conf on the cluster ui, will impact all notebooks attached to that cluster, for example i set the number of shuffle partitions to 4, and in every notebook when i inspect the number of partitions i find 4. upvoted 8 times

Selected Answer: D suretly D upvoted 1 times

Selected Answer: D D is correct. Point to Note: Precedence - (Spark configurations set programmatically in a notebook or script) > (Cluster-wide configurations set in the Clusters UI) upvoted 2 times

Selected Answer: D Both via API or UI will trigger a restart of the cluster so A is wrong. D is correct given that "impact" does not mean the cluster can't be restarted. upvoted 1 times

Selected Answer: B A. Incorrect: Modifying configurations through the Databricks REST API while jobs are running can lead to unexpected behavior or disruption. It's generally not recommended. C. Incorrect: While global init scripts can be used, it's not the only way. Configurations can also be set within notebooks. D. Incorrect: Configurations set through the Clusters UI apply to the entire cluster, but they might not necessarily override configurations set within notebooks attached to the cluster. E. Incorrect: Notebook configurations can take precedence over cluster-level configurations for the same property, offering finer-grained control at the notebook level. upvoted 1 times

Question 12 (Topic 1)

Which indicators would you look for in the Spark UI’s Storage tab to signal that a cached table is not performing optimally? Assume you are using Spark’s MEMORY_ONLY storage level.

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○ Size on Disk is < Size in Memory
○ The RDD Block Name includes the “*” annotation signaling a failure to cache
○ Size on Disk is > 0
○ The number of Cached Partitions > the number of Spark Partitions
○ On Heap Memory Usage is within 75% of Off Heap Memory Usage

Reference / correct answer:

Size on Disk is > 0

Most accepted answer: C. Size on Disk is > 0

Community votes: A=1, B=2, C=4

Selected Answer: C C. Size on Disk is > 0 When using Spark's MEMORY_ONLY storage level, the ideal scenario is that the data is fully cached in memory, and the Size on Disk should be 0 (indicating that the data is not spilled to disk). If the Size on Disk is greater than 0, it suggests that some data has been spilled to disk, which can lead to degraded performance as reading from disk is slower than reading from memory. upvoted 7 times

Selected Answer: B In the Spark UI’s Storage tab, when you're using the MEMORY_ONLY storage level, Spark tries to cache the RDD/table completely in memory. If a partition of the RDD does not fit into memory, Spark does not cache that partition and recomputes it when needed. Indicator: * in the Block Name A * (asterisk) next to an RDD block name in the Storage tab indicates that Spark failed to cache that block. upvoted 4 times

Selected Answer: B In the Spark UI's Storage tab, an indicator that a cached table is not performing optimally would be the presence of the _disk annotation in the RDD Block Name. This annotation indicates that some partitions of the cached data have been spilled to disk because there wasn't enough memory to hold them. This is suboptimal because accessing data from disk is much slower than from memory. The goal of caching is to keep data in memory for fast access, and a spill to disk means that this goal is not fully achieved. upvoted 1 times

Selected Answer: A Under MEMORY_ONLY, Spark does not write to disk, so Size on Disk should be 0. Under MEMORY_ONLY, off-heap memory is not used In the Storage tab, an asterisk (*) next to the RDD block name (e.g., rdd_42_3*) indicates the partition could not be cached due to memory constraints upvoted 1 times gloomy_marmot 1 year ago But it should be B upvoted 1 times ...

But it should be B upvoted 1 times

Free CERTIFIED-DATA-ENGINEER-PROFESSIONAL – Databricks CERTIFIED-DATA-ENGINEER-PROFESSIONAL Practice Test Question