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Pass Guaranteed Quiz Efficient Databricks - Databricks-Certified-Professional-Data-Engineer Certified Questions

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The Databricks Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and hands-on exercises designed to test the candidate's knowledge and skills in working with Databricks. Candidates who pass the exam will be awarded the Databricks Certified Professional Data Engineer certification, which is recognized by employers worldwide as a validation of the candidate's expertise and proficiency in building and maintaining data pipelines using Databricks. Overall, the Databricks Certified Professional Data Engineer certification exam is a valuable credential for anyone looking to advance their career in big data engineering and analytics.

Databricks Certified Professional Data Engineer exam consists of multiple-choice questions and is conducted online. Databricks-Certified-Professional-Data-Engineer Exam is intended to measure the candidate's proficiency in various areas, such as Spark architecture, Spark programming, data processing, data analysis, and data modeling. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize Spark performance and troubleshoot Spark applications. It is recommended that individuals who plan to take Databricks-Certified-Professional-Data-Engineer exam have at least two years of hands-on experience in big data technologies and Apache Spark.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

A. Databricks Repos allows multiple users to make changes
B. Databricks Repos allows merge and conflict resolution
C. Databricks Repos allow you to add comments and select the changes you want to commit.
D. Databricks Repos has a built-in version control system
E. Databricks Repos automatically saves changes

Answer: C

Explanation:
Explanation
The answer is Databricks Repos allow you to add comments and select the changes you want to commit.

NEW QUESTION # 79
Data engineering team has a job currently setup to run a task load data into a reporting table every day at 8: 00 AM takes about 20 mins, Operations teams are planning to use that data to run a second job, so they access latest complete set of data. What is the best to way to orchestrate this job setup?

A. Setup a second job to run at 8:20 AM in the same workspace
B. Add Operation reporting task in the same job and set the Data Engineering task to de-pend on Operations reporting task
C. Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task
D. Setup a Delta live to table based on the first table, set the job to run in continuous mode
E. Use Auto Loader to run every 20 mins to read the initial table and set the trigger to once and create a second job

Answer: C

Explanation:
Explanation
The answer is Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task.

Diagram Description automatically generated with medium confidence

NEW QUESTION # 80
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 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.
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 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.
D. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be 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. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 81
A data engineering team needs to query a Delta table to extract rows that all meet the same condi-tion.
However, the team has noticed that the query is running slowly. The team has already tuned the size of the
data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located
throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query?

A. Data skipping
B. Tuning the file size
C. Z-Ordering
D. Bin-packing
E. Write as a Parquet file

Answer: C

NEW QUESTION # 82
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Answer: B

Explanation:
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job1.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency2.
Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption3.
Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.

NEW QUESTION # 83
......

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