Databricks-Certified-Professional-Data-Engineer Valid Test Dumps | Databricks-Certified-Professional-Data-Engineer Exam Papers

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Data Processing with Spark25-30%- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions
- Python and SQL for data engineering
  • 1. Spark APIs in Python
  • 2. Performance optimization techniques
  • 3. Built-in and user-defined functions
Delta Lake20-25%- Delta Lake operations
  • 1. Delta Live Tables
  • 2. Schema evolution and enforcement
  • 3. Merge, update, delete operations
- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Optimize and Z-order
  • 3. Time travel and data versioning
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Data governance fundamentals
  • 2. Bronze, silver, gold data layers
  • 3. Differences between data lake, data warehouse, and lakehouse
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Monitoring and alerting
  • 2. Jobs and job scheduling
  • 3. Task dependencies and orchestration
Data Ingestion15-20%- Streaming ingestion
  • 1. Structured streaming fundamentals
  • 2. Kafka integration
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q167-Q172):

NEW QUESTION # 167
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch nameddev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

Answer: C

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-
2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.


NEW QUESTION # 168
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?

Answer: E

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 # 169
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

Answer: A

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.


NEW QUESTION # 170
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity.
Which SQL query should the team run from the System catalog to achieve this?
A)
SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B)
SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
C)
SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D)
SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non-existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.


NEW QUESTION # 171
Which of the following is true of Delta Lake and the Lakehouse?

Answer: E

Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters 1 . Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer 1 . By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan 1 . This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
* Parquet compresses data column by column, not row by row 2 . This allows for better compression ratios, especially for repeated or similar values within a column 2 .
* Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times 3 . Views are logical constructs that are defined by a SQL query on one or more base tables 3
. Views are not materialized by default, which means they do not store any data, but only the query definition 3 . Therefore, views always reflect the latest state of the source tables when queried 3 .
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
* Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
* Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping , Parquet Format , Views , [Caching], [Constraints] , [Z-Ordering]


NEW QUESTION # 172
......

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