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

Section	Weight	Objectives
Topic 1: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 2: Data Processing	28%	- ETL Pipelines - Spark SQL - Structured Streaming - Data Transformation
Topic 3: Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling
Topic 4: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Topic 5: Databricks Lakehouse Platform	24%	- Unity Catalog - Delta Lake - Data Management - Lakehouse Architecture

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q29-Q34):

質問 # 29
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
B. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
C. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
D. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.

正解：C

解説：
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

質問 # 30
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. Unit
B. End-to-end
C. functional
D. Manual
E. Integration

正解：A

解説：
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

質問 # 31
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
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Choose the response that correctly fills in the blank within the code block to complete this task.

A. to_interval("event_time", "5 minutes").alias("time")
B. lag("event_time", "10 minutes").alias("time")
C. "event_time"
D. window("event_time", "5 minutes").alias("time")
E. window("event_time", "10 minutes").alias("time")

正解：D

解説：
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

質問 # 32
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1

正解：C

解説：
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency. Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption. Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.

質問 # 33
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, during which you can export notebook run results to HTML
C. It is retained for 60 days, after which logs are archived
D. It is retained for 90 days or until the run-id is re-used through custom run configuration
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

正解：B

質問 # 34
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