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

SectionWeightObjectives
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Differences between data lake, data warehouse, and lakehouse
  • 2. Bronze, silver, gold data layers
  • 3. Data governance fundamentals
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Monitoring and alerting
  • 2. Task dependencies and orchestration
  • 3. Jobs and job scheduling
Delta Lake20-25%- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Optimize and Z-order
  • 3. Time travel and data versioning
- Delta Lake operations
  • 1. Delta Live Tables
  • 2. Schema evolution and enforcement
  • 3. Merge, update, delete operations
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Built-in and user-defined functions
  • 2. Spark APIs in Python
  • 3. Performance optimization techniques
- Spark DataFrames and Spark SQL
  • 1. DataFrame operations and transformations
  • 2. Spark SQL queries and functions
  • 3. Window functions
Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q73-Q78):

質問 # 73
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

正解：D

解説：
Explanation
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.
References:
https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge
https://docs.databricks.com/delta/delta-update.html#insert-only-merge


質問 # 74
A Delta Lake table was created with the below query:
Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

正解：E

解説：
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table


質問 # 75
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

正解：B

解説：
This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time- consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance thetrade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block.
Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Cluster configurations" section.


質問 # 76
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

正解：B

解説：
Explanation
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text.
Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "List secrets" section.


質問 # 77
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

正解：C


質問 # 78
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