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Databricks-Certified-Professional-Data-Engineer Test Assessment | Vce Databricks-Certified-Professional-Data-Engineer Exam

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. * Total VMs: 8
* 50 GB per Executor
* 20 Cores / Executor
B. * Total VMs; 1
* 400 GB per Executor
* 160 Cores / Executor

Answer: A

Explanation:
C.
* Total VMs: 4
* 100 GB per Executor
* 40 Cores/Executor
D.
* Total VMs:2
* 200 GB per Executor
* 80 Cores / Executor
Explanation:
This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance the trade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block. Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Cluster configurations" section.

NEW QUESTION # 48
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.mode("append").saveAsTable("churn_preds")
B.
C.
D.
E. preds.write.format("delta").save("/preds/churn_preds")

Answer: A

NEW QUESTION # 49
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Answer: E

Explanation:
Explanation
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 50
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete.
Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
B. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Catalog Explorer provides a feature called AI Generate that automatically produces intelligent comments for columns. This feature uses metadata such as column names, types, patterns, and sampled values to generate human-readable documentation. According to the documentation, this is the recommended method to rapidly enrich schema metadata and improve data discoverability, especially at enterprise scale. Unlike DESCRIBE HISTORY or DESCRIBE TABLE, which only surface technical schema details, AI Generate directly produces business-oriented descriptions. PySpark statistical functions (df.describe) only return numeric statistics and cannot generate descriptive metadata. Thus, AI Generate in Catalog Explorer is the correct approach.

NEW QUESTION # 51
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.
D. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible." Exact extract: "Trigger once processes all available data once and then stops." Exact extract: "Job clusters are created for a job run and terminate when the job completes." The default "as fast as possible" trigger creates many empty micro-batches which repeatedly list/query cloud storage, inflating storage/metadata API costs. Switching to trigger(once=True) and scheduling the job to run every 10 minutes processes all available data in one batch, then stops. This both meets the <10-minute freshness requirement and minimizes compute (cluster can shut down between runs) and storage API calls (one batch per run instead of continual empty batches).
Reference:

NEW QUESTION # 52
......

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