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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
| Topic 2: Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Topic 3: Delta Lake | 20-25% | - Delta Lake fundamentals
|
| Topic 4: Data Processing with Spark | 25-30% | - Spark DataFrames and Spark SQL
|
| Topic 5: Data Ingestion | 15-20% | - Streaming ingestion
|
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NEW QUESTION # 168
Which statement describes integration testing?
Answer: C
Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application.
Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.
NEW QUESTION # 169
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
Answer: E
Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl
dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html
NEW QUESTION # 170
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?
Answer: D
NEW QUESTION # 171
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "When using strings directly, Spark SQL interprets them as literal column names." Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate". The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.
NEW QUESTION # 172
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?
Answer: A
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 References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under
"Choose a region" section.
NEW QUESTION # 173
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