TEST DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER SCORE REPORT | PDF DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VERSION

Test Databricks Databricks-Certified-Professional-Data-Engineer Score Report | Pdf Databricks-Certified-Professional-Data-Engineer Version

Test Databricks Databricks-Certified-Professional-Data-Engineer Score Report | Pdf Databricks-Certified-Professional-Data-Engineer Version

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Databricks Certified Professional Data Engineer exam is a rigorous test that measures a candidate's knowledge and skills in various areas of Databricks, including data engineering, data modeling, data integration, data processing, and data storage. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to solve real-world problems using Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to assess the candidate's ability to design and implement scalable, reliable, and efficient data solutions that meet business requirements.

Databricks-Certified-Professional-Data-Engineer exam is a comprehensive assessment that evaluates a candidate's ability to design, implement, and manage data pipelines, as well as leverage advanced analytics and machine learning techniques on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and requires candidates to complete a hands-on project that demonstrates their ability to build a data solution on the Databricks platform.

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Databricks Certified Professional Data Engineer exam is a rigorous and comprehensive assessment of a candidate's skills in designing, building, and maintaining data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics, including data storage and retrieval, data processing, data transformation, and data visualization. Candidates are tested on their ability to design and implement scalable and reliable data architectures, as well as their proficiency in troubleshooting and optimizing data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

  • A. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
  • B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
  • C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
  • D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
  • E. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.

Answer: B

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints: https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions


NEW QUESTION # 64
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

  • A. Bytes Received never exceeds 80 million bytes per second
  • B. Total Disk Space remains constant
  • C. Network I/O never spikes
  • D. Overall cluster CPU utilization is around 25%
  • E. The five Minute Load Average remains consistent/flat

Answer: D

Explanation:
Explanation
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "View cluster status and event logs - Ganglia metrics" section; Databricks Documentation, under "Avoid collecting large RDDs" section.


NEW QUESTION # 65
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

  • A. The table reference in the metastore is updated and all data files are moved.
  • B. The table reference in the metastore is updated and no data is changed.
  • C. A new Delta transaction log Is created for the renamed table.
  • D. All related files and metadata are dropped and recreated in a single ACID transaction.
  • E. The table name change is recorded in the Delta transaction log.

Answer: B

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.


NEW QUESTION # 66
A table in the Lakehouse namedcustomer_churn_paramsis 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. 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.
  • B. 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.
  • 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. 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.
  • E. 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.

Answer: B

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.
References: Merge, Change data feed


NEW QUESTION # 67
What statement is true regarding the retention of job run history?

  • A. It is retained until you export or delete job run logs
  • B. It is retained for 60 days, after which logs are archived
  • C. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
  • D. It is retained for 90 days or until the run-id is re-used through custom run configuration
  • E. t is retained for 60 days, during which you can export notebook run results to HTML

Answer: E


NEW QUESTION # 68
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