Databricks Databricks-Certified-Data-Engineer-Professional考試資訊是行業領先材料＆Databricks-Certified-Data-Engineer-Professional考試資訊: Databricks Certified Data Engineer Professional Exam

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing	28%	- Data Transformation - Structured Streaming - Spark SQL - ETL Pipelines
Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Delta Lake - Data Management
Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality

>> Databricks-Certified-Data-Engineer-Professional考試資訊 <<

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q184-Q189):

問題 #184
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 for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
B. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

答案：C

解題說明：
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

問題 #185
A data engineering team uses Databricks Lakehouse Monitoring to track the percent_null metric for a critical column in their Delta table.
The profile metrics table (prod_catalog.prod_schema.customer_data_profile_metrics) stores hourly percent_null values.
The team wants to:
Trigger an alert when the daily average of percent_null exceeds 5% for
three consecutive days.
Ensure that notifications are not spammed during sustained issues.

A. SELECT SUM(CASE WHEN percent_null > 5 THEN 1 ELSE 0 END) AS violation_days FROM prod_catalog.prod_schema.customer_data_profile_metrics WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY Alert Condition: violation_days >= 3 Notification Frequency: Just once
B. SELECT percent_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '1' DAY
Alert Condition: percent_null > 5
Notification Frequency: At most every 24 hours
C. SELECT AVG(percent_null) AS daily_avg
FROM prod_catalog.prod_schema.customer_data_profile_metrics
WHERE window.end >= CURRENT_TIMESTAMP - INTERVAL '3' DAY
Alert Condition: daily_avg > 5
Notification Frequency: Each time alert is evaluated
D. WITH daily_avg AS (
SELECT DATE_TRUNC('DAY', window.end) AS day,
AVG(percent_null) AS avg_null
FROM prod_catalog.prod_schema.customer_data_profile_metrics
GROUP BY DATE_TRUNC('DAY', window.end)
)
SELECT day, avg_null
FROM daily_avg
ORDER BY day DESC
LIMIT 3
Alert Condition: ALL avg_null > 5 for the latest 3 rows
Notification Frequency: Just once

答案：D

解題說明：
The key requirement is to detect when the daily average of percent_null is greater than 5% for three consecutive days.
Option A only checks the last 24 hours, not consecutive days. It would trigger too frequently and cause spam.
Option C calculates an average across all records in the last 3 days, but this could be skewed by one high or low day -- it does not ensure consecutive daily violations.
Option D simply counts days where the threshold was exceeded, but it does not guarantee that those days were consecutive. This could incorrectly trigger on non-adjacent violations.
Option B is correct:
It aggregates hourly values into daily averages.
It checks that the last 3 consecutive days all had averages above 5%.
It avoids redundant alerts by using Notification Frequency: Just once.
This matches Databricks Lakehouse Monitoring best practices, where SQL alerts should be designed to aggregate metrics to the correct granularity (daily here) and ensure consecutive threshold violations before triggering.

問題 #186
What statement is true regarding the retention of job run history?

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

答案：D

問題 #187
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. userLookup.join(streamingDF, ["user_id"], how="right")
B. streamingDF.join(userLookup, ["user_id"], how="outer")
C. streamingDF.join(userLookup, ["userid"], how="inner")
D. userLookup.join(streamingDF, ["userid"], how="inner")
E. streamingDF.join(userLookup, ["user_id"], how="left")

答案：B

解題說明：
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

問題 #188
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Executor's log file
B. Ganglia
C. Driver's log file
D. Workspace audit logs
E. Cluster Event Log

答案：E

問題 #189
......

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Databricks-Certified-Data-Engineer-Professional證照指南: https://www.kaoguti.com/Databricks-Certified-Data-Engineer-Professional_exam-pdf.html