Databricks-Certified-Data-Engineer-Professional試験解説問題、Databricks-Certified-Data-Engineer-Professional無料試験

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

Section	Weight	Objectives
Topic 1: Data Sharing and Federation	5%	- Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing
Topic 2: Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources
Topic 3: Data Governance	7%	- Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata
Topic 4: Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code
Topic 5: Data Modelling	6%	- Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models
Topic 6: Monitoring and Alerting	10%	- Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics
Topic 7: Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance
Topic 8: Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards
Topic 9: Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines
Topic 10: Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources

>> Databricks-Certified-Data-Engineer-Professional試験解説問題 <<

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q233-Q238):

質問 # 233
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. Network I/O never spikes
B. Total Disk Space remains constant
C. Bytes Received never exceeds 80 million bytes per second
D. Overall cluster CPU utilization is around 25%
E. The five Minute Load Average remains consistent/flat

正解：D

解説：
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver.

質問 # 234
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

正解：C

解説：
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

質問 # 235
What is the first of a Databricks Python notebook when viewed in a text editor?

A. %python
B. # MAGIC %python
C. # Databricks notebook source
D. // Databricks notebook source
E. -- Databricks notebook source

正解：C

解説：
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

質問 # 236
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?

A. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
B. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
C. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
D. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
E. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

正解：A

解説：
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

質問 # 237
A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A. Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B. Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
C. Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
D. When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.

正解：C

解説：
Reading the change data feed from the customer_dim table enables downstream processing to react specifically to update and delete events while operating incrementally. Change data feed exposes row-level change types and versions, making it the correct mechanism for streaming only the relevant changes from an SCD Type 1 table maintained with AUTO CDC INTO.

質問 # 238
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