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Databricks Certified Data Engineer Professional Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which statement describes Delta Lake optimized writes?

A. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

Answer: A

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 14
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
B. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
C. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
D. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from made based upon what is most convenient for the workspace administrator.

Answer: B

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.

NEW QUESTION # 15
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 run
C. Can Read
D. Can edit

Answer: C

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.

NEW QUESTION # 16
A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
B. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
C. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
D. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
E. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

Answer: E

Explanation:
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed.
This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.

NEW QUESTION # 17
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Driver's and Executor's log files
B. Executor's detail screen and Executor's log files
C. Query's detail screen and Job's detail screen
D. Stage's detail screen and Query's detail screen
E. Stage's detail screen and Executor's log files

Answer: E

Explanation:
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.

NEW QUESTION # 18
......

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Databricks-Certified-Data-Engineer-Professional Valid Exam Format & Exam Databricks-Certified-Data-Engineer-Professional Simulator Fee

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q13-Q18):

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