Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps

Databricks Certified Data Engineer Professional Exam

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Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions

Question # 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?

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

Answer: D

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Question # 2

Review the following error traceback: Which statement describes the error being raised? 

A. The code executed was PvSoark but was executed in a Scala notebook. 
B. There is no column in the table named heartrateheartrateheartrate 
C. There is a type error because a column object cannot be multiplied. 
D. There is a type error because a DataFrame object cannot be multiplied. 
E. There is a syntax error because the heartrate column is not correctly identified as a column. 

Answer: E

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Question # 3

In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected. The function is displayed below with a blank: Which response correctly fills in the blank to meet the specified requirements? 

A. Option A 
B. Option B 
C. Option C 
D. Option D 
E. Option E 

Answer: B

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Question # 4

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: Choose the response that correctly fills in the blank within the code block to complete this task. 

A. to_interval("event_time", "5 minutes").alias("time") 
B. window("event_time", "5 minutes").alias("time") 
C. "event_time" 
D. window("event_time", "10 minutes").alias("time") E. lag("event_time", "10 minutes").alias("time") 

Answer: B

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Question # 5

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, builtin file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used. Which strategy will yield the best performance without shuffling data? 

A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet. 
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet. 
C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet. 
D. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet. 
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet. 

Answer: B

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Question # 6

A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job: Which statement describes the execution and results of running the above query multiple times? 

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys. 
B. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries. 
C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result. 
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records. 
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result. 

Answer: B

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Question # 7

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively. Which of the following adjustments will get a more accurate measure of how code is likely to perform in production? 

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored. 
B. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution. 
C. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production. 
D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. 
E. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs. 

Answer: D

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Question # 8

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure. The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications. The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process? 

A. The Tungsten encoding used by Databricks is optimized for storing string data; newlyadded native support for querying JSON strings means that string types are always most efficient. 
B. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place. 
C. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads. 
D. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement. E. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems. 

Answer: D

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Question # 9

The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema: For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields: Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization. Which solution meets the expectations of the end users while controlling and limiting possible costs? 

A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query. 
B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query. 
C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook. 
D. Define a view against the products_per_order table and define the dashboard against this view. 

Answer: D

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Question # 10

A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI? 

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command 
B. Stop the existing pipeline; use the returned settings in a reset command 
C. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git 
D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline 

Answer: A

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