Databricks-Certified-Professional-Data-Engineer学習関連題 & Databricks-Certified-Professional-Data-Engineerテスト模擬問題集

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

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

>> Databricks-Certified-Professional-Data-Engineer学習関連題 <<

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q163-Q168):

質問 # 163
What is the underlying technology that makes the Auto Loader work?

A. DataFrames
B. Structured Streaming
C. Delta Live Tables
D. Loader
E. Live DataFames

正解：B

質問 # 164
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
E. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

正解：A

解説：
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.

質問 # 165
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
C. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
D. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

正解：E

解説：
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines. Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field wasomitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delta Lake core features" section.

質問 # 166
One of the team members Steve who has the ability to create views, created a new view called re-gional_sales_vw on the existing table called sales which is owned by John, and the second team member Kevin who works with regional sales managers wanted to query the data in region-al_sales_vw, so Steve granted the permission to Kevin using command GRANT VIEW, USAGE ON regional_sales_vw to kevin@company.com but Kevin is still unable to access the view?

A. Steve is not the owner of the sales table
B. Table access control is not enabled on the table and view
C. Kevin is not the owner of the sales table
D. Kevin needs owner access on the view regional_sales_vw
E. Kevin needs select access on the table sales

正解：A

解説：
Explanation
Ownership determines whether or not you can grant privileges on derived objects to other users, since Steve is not the owner of the underlying sales table, he can not grant access to the table or data in the table indirectly.
Only owner(user or group) can grant access to a object
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#a-user-has Data object privileges - Azure Databricks | Microsoft Doc

質問 # 167
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. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. withWatermark("event_time", "10 minutes")
B. slidingWindow("event_time", "10 minutes")
C. delayWrite("event_time", "10 minutes")
D. await("event_time + '10 minutes'")
E. awaitArrival("event_time", "10 minutes")

正解：A

解説：
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

質問 # 168
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