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Databricks is a cloud-based data processing and analytics platform that is widely used in the industry. It provides an end-to-end solution for data engineering, machine learning, and business analytics. To validate the skills and expertise of data engineers who work with Databricks, the company offers a certification program known as the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) exam. Databricks Certified Professional Data Engineer Exam certification is designed to help data engineers demonstrate their proficiency in using Databricks to design, build, and maintain data pipelines and data solutions.

To prepare for the DCPDE exam, candidates should have a solid understanding of data engineering concepts, such as data modeling, data integration, data transformation, and data quality. They should also have experience working with big data technologies, such as Apache Spark, Apache Kafka, and Apache Hadoop.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A. CPU Utilization is around 75%
B. Total Disk Space remains constant
C. Network I/O never spikes
D. Bytes Received never exceeds 80 million bytes per second
E. The five Minute Load Average remains consistent/flat

Answer: A

Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around 75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized.
* A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation.
* A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
* Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
* Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
References:
* Ganglia Monitoring System: Ganglia Documentation
* Databricks Documentation on Monitoring: Databricks Cluster Monitoring

NEW QUESTION # 86
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

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

Answer: D

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 # 87
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

A. CACHE TABLE table_name
B. CLEAR CACH table_name
C. BROADCAST TABLE table_name
D. UNCACHE TABLE table_name
E. REFRESH TABLE table_name

Answer: E

Explanation:
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.

NEW QUESTION # 88
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

A. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
B. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
C. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
D. CREATE DATABASE customer360 LOCATION '/customer/customer360';
E. CREATE DATABASE IF NOT EXISTS customer360;

Answer: B

NEW QUESTION # 89
Which of the following technique can be used to implement fine-grained access control to rows and columns of the Delta table based on the user's access?

A. Use Unity catalog to grant access to rows and columns
B. Dynamic Access control lists with Unity Catalog
C. Use dynamic view functions
D. Row and column access control lists
E. Data access control lists

Answer: C

Explanation:
Explanation
The answer is, Use dynamic view functions.
Here is an example that limits access to rows based on the user being part managers group, in the below view if a user is not a part of the manager's group you can only see rows where the total amount is <= 1000000 Dynamic view function to filter rows
1.CREATE VIEW sales_redacted AS
2.SELECT user_id, country, product, total
3.FROM sales_raw
4.WHERE CASE WHEN is_member('managers') THEN TRUE ELSE total <= 1000000 END; Dynamic view function to hide a column data based on user's access,
1.CREATE VIEW sales_redacted AS
2.SELECT user_id,
3. CASE WHEN is_member('auditors') THEN email ELSE 'REDACTED' END AS email,
4. country,
5. product,
6. total
7.FROM sales_raw
Please review below for more details
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#dynamic-v

NEW QUESTION # 90
......

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