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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: Data Ingestion | 15-20% | - Batch ingestion methods
|
| Topic 4: Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Topic 5: Delta Lake | 20-25% | - Delta Lake fundamentals
|
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NEW QUESTION # 210
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should havefive workers,one driverof type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
* Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).
* Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.
* Workers:--num-workers 5 sets the number of worker nodes to 5.
* Node Type:--node-type-id i3.xlarge defines the hardware configuration.
* Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
* Option A (databricks clusters create ...)
* Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.
* The correct CLI command for cluster creation is databricks compute create.
* Option B (databricks clusters add ...)
* Incorrect:databricks clusters addis not a valid CLI command.
* Option C (databricks compute add ...)
* Incorrect:databricks compute addis not a valid CLI command.
* Option D (databricks compute create ...)
* Correct:databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime
14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer isD.
References:
* Databricks CLI Documentation
NEW QUESTION # 211
A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
According to Databricks documentation, Change Data Feed (CDF) allows pipelines to read incremental data changes, including inserts, updates, and deletes, from a Delta table. When deletions occur in the source table, reading the CDF stream ensures downstream consumers receive the deletion records. The Lakeflow Declarative Pipelines API provides the apply_changes() function (or auto-CDC pipelines) with the apply_as_deletes parameter to correctly apply those deletions to the target table. This enables automatic synchronization between bronze and silver layers. Options A and D either require manual handling or complete rebuilds, and C incorrectly applies CDF to the target rather than the source. Therefore, enabling CDF on the bronze table and using apply_as_deletes=True is the correct, Databricks-supported configuration.
NEW QUESTION # 212
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
Answer: A
Explanation:
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.
NEW QUESTION # 213
Which of the following commands can be used to run one notebook from another notebook?
Answer: C
Explanation:
Explanation
The answer is dbutils.notebook.run(" full notebook path ")
Here is the full command with additional options.
run(path: String, timeout_seconds: int, arguments: Map): String
1.dbutils.notebook.run("ful-notebook-name", 60, {"argument": "data", "argument2": "data2", ...})
NEW QUESTION # 214
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
Answer: D
Explanation:
Explanation
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html
NEW QUESTION # 215
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