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Simulated Databricks-Certified-Professional-Data-Engineer Test, Reliable Databricks-Certified-Professional-Data-Engineer Test Experience

Simulated Databricks-Certified-Professional-Data-Engineer Test, Reliable Databricks-Certified-Professional-Data-Engineer Test Experience

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q128-Q133):

NEW QUESTION # 128
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

  • A. The source query failed to update properly for three consecutive minutes and then restarted
  • B. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
  • C. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
  • D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
  • E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

Answer: B

Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.


NEW QUESTION # 129
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should havefive workers,one driverof type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?

  • A. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name DataEngineer_cluster
  • B. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
  • C. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
  • D. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
* Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).
* Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.
* Workers:--num-workers 5 sets the number of worker nodes to 5.
* Node Type:--node-type-id i3.xlarge defines the hardware configuration.
* Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
* Option A (databricks clusters create ...)
* Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.
* The correct CLI command for cluster creation is databricks compute create.
* Option B (databricks clusters add ...)
* Incorrect:databricks clusters addis not a valid CLI command.
* Option C (databricks compute add ...)
* Incorrect:databricks compute addis not a valid CLI command.
* Option D (databricks compute create ...)
* Correct:databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime
14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer isD.
References:
* Databricks CLI Documentation


NEW QUESTION # 130
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

  • A. Yes; both of the streams can share a single checkpoint directory.
  • B. No; only one stream can write to a Delta Lake table.
  • C. No; each of the streams needs to have its own checkpoint directory.
  • D. Yes; Delta Lake supports infinite concurrent writers.
  • E. No; Delta Lake manages streaming checkpoints in the transaction log.

Answer: C

Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Checkpointing" section.


NEW QUESTION # 131
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

  • A.
  • B. return spark.readStream.load("bronze")
  • C. return spark.readStream.table("bronze")
  • D.
  • E. return spark.read.option("readChangeFeed", "true").table ("bronze")

Answer: A

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html


NEW QUESTION # 132
A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

  • A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
  • B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
  • C. Modify the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
  • D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
  • E. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

Answer: E

Explanation:
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed. This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table12. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
Option A would require applying the churn model to all rows in the customer_churn_params table, which would be wasteful and redundant. It would also require implementing logic to perform an upsert into the predictions table, which would be more complex than using the merge statement.
Option B would require converting the batch job to a Structured Streaming job, which would involve changing the data ingestion and processing logic. It would also require using the complete output mode, which would output the entire result table every time there is a change in the source data, which would be inefficient and costly.
Option C would require calculating the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers, which would be computationally expensive and prone to errors. It would also require storing and accessing the previous predictions, which would add extra storage and I/O costs.
Option D would require modifying the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written, which would add extra complexity and overhead to the data engineering job. It would also require using this field to identify records written on a particular date, which would be less accurate and reliable than using the change data feed.


NEW QUESTION # 133
......

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