DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL TRAINING MATERIALS ARE WORTHY FOR YOU TO BUY IT - BRAINDUMPSIT

Databricks-Certified-Data-Engineer-Professional Training Materials are Worthy for You to Buy It - BraindumpsIT

Databricks-Certified-Data-Engineer-Professional Training Materials are Worthy for You to Buy It - BraindumpsIT

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

  • A. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
  • B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
  • C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
  • D. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
  • E. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Answer: A

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running.


NEW QUESTION # 20
A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

  • A. They are ignored.
  • B. They are merged.
  • C. They are inserted.
  • D. They are deleted.
  • E. They are updated.

Answer: A

Explanation:
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events.


NEW QUESTION # 21
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

  • A. All records are cached to an operational database and then the filter is applied
  • B. All records are cached to attached storage and then the filter is applied Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
  • C. The Parquet file footers are scanned for min and max statistics for the latitude column
  • D. The Hive metastore is scanned for min and max statistics for the latitude column
  • E. The Delta log is scanned for min and max statistics for the latitude column

Answer: E

Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.


NEW QUESTION # 22
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

  • A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
  • B. The connection to the external table will fail; the string "redacted" will be printed.
  • C. The connection to the external table will succeed; the string "redacted" will be printed.
  • D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
  • E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Answer: C

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.


NEW QUESTION # 23
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

  • A. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
  • B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
  • C. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
  • D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
  • E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

Answer: C

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.


NEW QUESTION # 24
......

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