Databricks-Certified-Data-Engineer-Professional合格体験談 & Databricks-Certified-Data-Engineer-Professionalテストサンプル問題

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

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Unity Catalog - Delta Lake - Lakehouse Architecture - Data Management
Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling
Data Quality and Governance	12%	- Governance - Data Quality - Data Lineage
Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Data Processing	28%	- Structured Streaming - Spark SQL - ETL Pipelines - Data Transformation

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q212-Q217):

質問 # 212
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The update throws an error because changes to existing columns in the target schema are not supported.
C. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

正解：C

解説：
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

質問 # 213
Which distribution does Databricks support for installing custom Python code packages?

A. CRAN
B. jars
C. sbt
D. Wheels
E. CRAM
F. nom

正解：F

解説：
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows

質問 # 214
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
C. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
D. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

正解：E

解説：
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

質問 # 215
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
D. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
E. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

正解：C

解説：
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

質問 # 216
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
C. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
D. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
E. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

正解：C

質問 # 217
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