Databricks Databricks-Certified-Professional-Data-Engineer Questions - New Databricks-Certified-Professional-Data-Engineer Test Syllabus

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Databricks Certified Professional Data Engineer is a certification exam that tests the skills and knowledge required to design and implement data solutions using Databricks. Databricks is a cloud-based data platform that helps organizations manage and process large amounts of data. Databricks Certified Professional Data Engineer Exam certification exam is designed for data engineers who are responsible for creating and maintaining data pipelines, managing data storage, and implementing data solutions.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

A. DELETE FROM transactions where transactionDate > current_timestamp()
B. DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY
C. DELETE FROM transactions if transctionDate > current_timestamp()
D. DELET FROM transactions where transactionDate GE current_timestamp()
E. DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()

Answer: A

NEW QUESTION # 61
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
E. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

Answer: C

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 62
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. 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. 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. 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.
E. 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.

Answer: B

Explanation:
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 # 63
Which statement describes the default execution mode for Databricks Auto Loader?

A. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
C. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
D. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.

Answer: D

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake. The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.
References:
* Databricks Auto Loader Documentation: Auto Loader Guide
* Delta Lake and Auto Loader: Delta Lake Integration

NEW QUESTION # 64
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
B. Configure a job that executes every time new data lands in a given directory.
C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D. Schedule a job to execute the pipeline once hour on a new job cluster.

Answer: D

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
Reference:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html

NEW QUESTION # 65
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