Question # 1
| A data architect has designed a system in which two Structured Streaming jobs will
concurrently write to a single bronze Delta table. Each job is subscribing to a different topic
from an Apache Kafka source, but they will write data with the same schema. To keep the
directory structure simple, a data engineer has decided to nest a checkpoint directory to be
shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given
scenario and why?
| | A. No; Delta Lake manages streaming checkpoints in the transaction log. | | B. Yes; both of the streams can share a single checkpoint directory. | | C. No; only one stream can write to a Delta Lake table. | | D. Yes; Delta Lake supports infinite concurrent writers. | | E. No; each of the streams needs to have its own checkpoint directory. |
E. No; each of the streams needs to have its own checkpoint directory.
Explanation:
This is the correct answer because checkpointing is a critical feature of
Structured Streaming that provides fault tolerance and recovery in case of failures.
Checkpointing stores the current state and progress of a streaming query in a reliable
storage system, such as DBFS or S3. Each streaming query must have its own checkpoint
directory that is unique and exclusive to that query. If two streaming queries share the
same checkpoint directory, they will interfere with each other and cause unexpected errors
or data loss. Verified References: [Databricks Certified Data Engineer Professional], under
“Structured Streaming” section; Databricks Documentation, under “Checkpointing” section.
Question # 2
| A data architect has heard about lake's built-in versioning and time travel capabilities. For
auditing purposes they have a requirement to maintain a full of all valid street addresses as
they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with
new values and relying on Delta Lake time travel to support long-term auditing. A data
engineer on the project feels that a Type 2 table will provide better performance and
scalability.
Which piece of information is critical to this decision? | | A. Delta Lake time travel does not scale well in cost or latency to provide a long-term
versioning solution. | | B. Delta Lake time travel cannot be used to query previous versions of these tables
because Type 1 changes modify data files in place. | | C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for
long-term versioning. | | D. Data corruption can occur if a query fails in a partially completed state because Type 2
tables requires
Setting multiple fields in a single update. |
A. Delta Lake time travel does not scale well in cost or latency to provide a long-term
versioning solution.
Explanation:
Delta Lake's time travel feature allows users to access previous versions of a
table, providing a powerful tool for auditing and versioning. However, using time travel as a
long-term versioning solution for auditing purposes can be less optimal in terms of cost and
performance, especially as the volume of data and the number of versions grow. For
maintaining a full history of valid street addresses as they appear in a customers table,
using a Type 2 table (where each update creates a new record with versioning) might
provide better scalability and performance by avoiding the overhead associated with
accessing older versions of a large table. While Type 1 tables, where existing records are
overwritten with new values, seem simpler and can leverage time travel for auditing, the
critical piece of information is that time travel might not scale well in cost or latency for longterm versioning needs, making a Type 2 approach more viable for performance and
scalability.References:
Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel
Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake:
Managing SCDs in Delta Lake
Question # 3
| The data engineer team is configuring environment for development testing, and production
before beginning migration on a new data pipeline. The team requires extensive testing on
both the code and data resulting from code execution, and the team want to develop and
test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development
testing environments, allowing pre production code to execute against production data.
Because all users have
Admin privileges in the development environment, the junior data engineer has offered to
configure permissions and mount this data for the team.
Which statement captures best practices for this situation? | | A. Because access to production data will always be verified using passthrough credentials
it is safe to mount data to any Databricks development environment. | | B. All developer, testing and production code and data should exist in a single unified
workspace; creating separate environments for testing and development further reduces
risks. | | C. In environments where interactive code will be executed, production data should only be
accessible with read permissions; creating isolated databases for each environment further
reduces risks. | | D. Because delta Lake versions all data and supports time travel, it is not possible for user
error or malicious actors to permanently delete production data, as such it is generally safe
to mount production data anywhere. |
C. In environments where interactive code will be executed, production data should only be
accessible with read permissions; creating isolated databases for each environment further
reduces risks.
Explanation:
The best practice in such scenarios is to ensure that production data is
handled securely and with proper access controls. By granting only read access to
production data in development and testing environments, it mitigates the risk of
unintended data modification. Additionally, maintaining isolated databases for different
environments helps to avoid accidental impacts on production data and systems.
References:
Databricks best practices for securing data:
https://docs.databricks.com/security/index.html
Question # 4
| A Delta Lake table representing metadata about content from user has the following
schema:
Based on the above schema, which column is a good candidate for partitioning the Delta
Table? | | A. Date | | B. Post_id | | C. User_id | | D. User_id | | E. Post_time |
A. Date
Explanation:
Partitioning a Delta Lake table improves query performance by organizing
data into partitions based on the values of a column. In the given schema, the date column
is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by
the date column can significantly improve performance by limiting the amount of
data scanned.
Granularity: The date column likely has a granularity that leads to a reasonable
number of partitions (not too many and not too few). This balance is important for
optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition
sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to
its more manageable granularity.
References:
Delta Lake Documentation on Table Partitioning: Optimizing Layout with
Partitioning
Question # 5
The Databricks workspace administrator has configured interactive clusters for each of the
data engineering groups. To control costs, clusters are set to terminate after 30 minutes of
inactivity. Each user should be able to execute workloads against their assigned clusters at
any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which
of the following describes the minimal permissions a user would need to start and attach to
an already configured cluster.
| | A. "Can Manage" privileges on the required cluster | | B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the
required cluster | | C. Cluster creation allowed. "Can Attach To" privileges on the required cluster | | D. "Can Restart" privileges on the required cluster | | E. Cluster creation allowed. "Can Restart" privileges on the required cluster |
D. "Can Restart" privileges on the required cluster
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/authauthz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html
Question # 6
| Incorporating unit tests into a PySpark application requires upfront attention to the design
of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort? | | A. Improves the quality of your data | | B. Validates a complete use case of your application | | C. Troubleshooting is easier since all steps are isolated and tested individually | | D. Yields faster deployment and execution times | | E. Ensures that all steps interact correctly to achieve the desired end result |
A. Improves the quality of your data
Question # 7
| 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. The five Minute Load Average remains consistent/flat | | B. Bytes Received never exceeds 80 million bytes per second | | C. Network I/O never spikes | | D. Total Disk Space remains constant | | E. CPU Utilization is around 75% |
E. CPU Utilization is around 75%
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
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FAQs of Databricks-Certified-Professional-Data-Engineer Exam
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There
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