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| Section | Weight | Objectives |
|---|---|---|
| Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices |
| Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance |
| Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Set up alerts and notifications - Track data lineage and metrics |
| Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Data Sharing and Federation | 5% | - Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing |
| Data Modelling | 6% | - Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning |
>> Databricks-Certified-Data-Engineer-Professional最新試題 <<
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問題 #38
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?
答案：A
解題說明：
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator.
問題 #39
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
答案：B
解題說明：
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.
問題 #40
A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)
答案：C,D
解題說明：
Liquid Clustering replaces traditional partitioning and ZORDER optimization by automatically organizing data according to clustering keys. It supports evolving clustering strategies without requiring a full table rewrite. To maintain cluster balance and improve performance, the OPTIMIZE command should be run periodically. OPTIMIZE groups data files by clustering keys and helps reduce small file overhead.
問題 #41
Which method can be used to determine the total wall-clock time it took to execute a query?
答案：A
解題說明：
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs.
Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.
問題 #42
Which of the following is true of Delta Lake and the Lakehouse?
答案：A
解題說明：
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.
問題 #43
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