Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Questions & Answers

• Question 121:

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

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

• Question 122:

  Which is a key benefit of an end-to-end test?

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

• Question 123:

  The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.

  What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

  A. Can manage
  B. Can edit
  C. Can run
  D. Can Read
  D. Can Read

• Question 124:

  Which statement describes Delta Lake Auto Compaction?

  A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
  B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
  C. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
  D. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
  E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

• Question 125:

  A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:

  SELECT COUNT (*) FROM table Which of the following describes how results are generated each time the dashboard is updated?

  A. The total count of rows is calculated by scanning all data files
  B. The total count of rows will be returned from cached results unless REFRESH is run
  C. The total count of records is calculated from the Delta transaction logs
  D. The total count of records is calculated from the parquet file metadata
  E. The total count of records is calculated from the Hive metastore
  C. The total count of records is calculated from the Delta transaction logs

• Question 126:

  An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:

  df = spark.read.format("parquet").load(f"/mnt/source/(date)")

  Which code block should be used to create the date Python variable used in the above code block?

  A. date = spark.conf.get("date")
  B. input_dict = input() date= input_dict["date"]
  C. import sys date = sys.argv[1]
  D. date = dbutils.notebooks.getParam("date")
  E. dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")
  E. dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")

• Question 127:

  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 abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

  

  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 entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
  C. 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.
  D. 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.
  E. 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.
  B. 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.