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Databricks Certified Data Engineer Professional Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
The data engineering team maintains the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
B. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
C. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
D. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
E. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

Answer: B

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed.

NEW QUESTION # 33
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: A

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 34
A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Post_id
B. Post_time
C. User_id
D. Date
E. latitude

Answer: D

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 35
A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

A. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
B. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.
C. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
D. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.

Answer: C

Explanation:
Lakeflow Spark Declarative Pipelines provide built-in support for data quality enforcement using EXPECT clauses. By defining expectations on critical fields and configuring the pipeline to audit or quarantine failed records, malformed or non-conforming JSON records are automatically routed to a quarantine Delta table while valid records continue flowing into the Bronze layer. This approach is robust, auditable, works for both batch and streaming ingestion, and avoids silently dropping bad data or failing the pipeline.

NEW QUESTION # 36
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. Stop the existing pipeline; use the returned settings in a reset command
B. 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
C. 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
D. 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

Answer: D

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

NEW QUESTION # 37
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Databricks-Certified-Data-Engineer-Professional Valid Dumps Demo - Certification Databricks-Certified-Data-Engineer-Professional Exam Infor

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q32-Q37):

Databricks-Certified-Data-Engineer-Professional Exam Questions - Databricks Certified Data Engineer Professional Exam Exam Tests & Databricks-Certified-Data-Engineer-Professional Test Guide

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