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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q44-Q49):

問題 #44
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
B. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
C. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
D. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
E. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

答案：A

問題 #45
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
D. Instruct the marketing team to download results as a CSV and email them to the sales organization.
E. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.

答案：B

解題說明：
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

問題 #46
Which statement describes a key benefit of an end-to-end test?

A. It provides testing coverage for all code paths and branches.
B. It pinpoint errors in the building blocks of your application.
C. It closely simulates real world usage of your application.
D. It makes it easier to automate your test suite

答案：C

解題說明：
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

問題 #47
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
B. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

答案：D

解題說明：
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

問題 #48
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.adaptive.coalescePartitions.minPartitionNum
B. spark.sql.files.maxPartitionBytes
C. spark.sql.files.openCostInBytes
D. spark.sql.autoBroadcastJoinThreshold
E. spark.sql.adaptive.advisoryPartitionSizeInBytes

答案：B

解題說明：
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

問題 #49
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