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

Section	Weight	Objectives
Topic 1: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 2: Data Governance	7%	- Unity Catalog management - Data lineage and metadata tracking - Policy enforcement
Topic 3: Ensuring Data Security and Compliance	10%	- Access control and permissions - Compliance standards implementation - Data encryption and masking
Topic 4: Cost & Performance Optimisation	13%	- Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling
Topic 5: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs
Topic 6: Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Topic 7: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic
Topic 8: Monitoring and Alerting	10%	- Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging
Topic 9: Data Ingestion & Acquisition	7%	- Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion
Topic 10: Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q69-Q74):

問題 #69
Which of the following python statements can be used to replace the schema name and table name in the query?

A. 1.table_name = "sales"
2.query = f"select * from + schema_name +"."+table_name"
B. 1.table_name = "sales"
2.query = "select * from {schema_name}.{table_name}"
C. 1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
D. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from schema_name.table_name"

答案：C

解題說明：
Explanation
The answer is
1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
It is always best to use f strings to replace python variables, rather than using string concatenation.

問題 #70
A data engineer is designing a data pipeline in Databricks that needs to process records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Use an Auto CDC pipeline with batch tables to simplify late data handling.
B. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
C. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
D. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.

答案：C

解題說明：
Databricks and Apache Spark document watermarks as the standard mechanism for handling late-arriving data in Structured Streaming. A watermark defines how long the engine should continue waiting for out-of- order event-time data and helps manage state for aggregations, joins, and deduplication. ( Databricks Documentation ) This directly matches Kafka streaming scenarios where lateness is common. The other options are not the standard streaming solution for event-time late data: Auto CDC is for change data capture use cases, and repeated full historical reprocessing is inefficient and unnecessary when watermarking is the built-in feature designed for this problem. ( Databricks Documentation )
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問題 #71
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?

答案：B

解題說明：
When joining a stream of advertisement impressions with a stream of user clicks, you want to 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.

問題 #72
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales .

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
B. Both commands will fail. No new variables, tables, or views will be created.
C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
D. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af:
if this entity exists, Cmd 2 will succeed.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

答案：C

解題說明：
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.

問題 #73
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

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

答案：D

解題說明：
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.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
Reference:
Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning

問題 #74
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