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Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps
Certified Data Engineer Professional Questions 137–144 (page: 19)

Updated On: 16-Apr-2026
Page 19 of 44
PDF with 339 Questions

Which statement regarding stream-static joins and static Delta tables is correct?

  1. The checkpoint directory will be used to track updates to the static Delta table.
  2. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
  3. The checkpoint directory will be used to track state information for the unique keys present in the join.
  4. Stream-static joins cannot use static Delta tables because of consistency issues.

Answer(s): B


Reference:



A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Events are recorded once per minute per device.

Streaming DataFrame df has the following schema:

"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

Code block:



Which line of code correctly fills in the blank within the code block to complete this task?

  1. to_interval("event_time", "5 minutes").alias("time")
  2. window("event_time", "5 minutes").alias("time")
  3. "event_time"
  4. lag("event_time", "10 minutes").alias("time")

Answer(s): B



A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.

Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

  1. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
  2. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
  3. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
  4. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

Answer(s): B


Reference:



Which statement describes Delta Lake optimized writes?

  1. Before a Jobs cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
  2. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 1 G
  3. A shuffle occurs prior to writing to try to group similar data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
  4. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer(s): C


Reference:



Which statement characterizes the general programming model used by Spark Structured Streaming?

  1. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
  2. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
  3. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
  4. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded
    table.

Answer(s): D


Reference:



Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

  1. spark.sql.files.maxPartitionBytes
  2. spark.sql.autoBroadcastJoinThreshold
  3. spark.sql.adaptive.advisoryPartitionSizeInBytes
  4. spark.sql.adaptive.coalescePartitions.minPartitionNum

Answer(s): A


Reference:



A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.

Which situation is causing increased duration of the overall job?

  1. Task queueing resulting from improper thread pool assignment.
  2. Spill resulting from attached volume storage being too small.
  3. Network latency due to some cluster nodes being in different regions from the source data
  4. Skew caused by more data being assigned to a subset of spark-partitions.

Answer(s): D



Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.

Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

  1. · Total VMs: 8
    · 50 GB per Executor
    · 20 Cores / Executor
  2. · Total VMs: 16
    · 25 GB per Executor
    · 10 Cores / Executor
  3. · Total VMs: 1
    · 400 GB per Executor
    · 160 Cores/Executor
  4. · Total VMs: 4
    · 100 GB per Executor
    · 40 Cores / Executor

Answer(s): B


Reference:



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