Databricks-Certified-Data-Engineer-Associate Fragen Und Antworten - Databricks-Certified-Data-Engineer-Associate Schulungsunterlagen

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

Section	Weight	Objectives
Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Apache Spark Data Processing Fundamentals	20-25%	- Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Apply transformations and actions on DataFrames
Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Aggregate and group data - Write and execute Spark SQL queries - Join and union DataFrames
Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts
Data Pipeline Architecture	15-20%	- Implement incremental data processing - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance
Delta Lake Fundamentals	20-25%	- Understand ACID transactions and time travel - Create and manage Delta tables - Explain Delta Lake features and benefits - Write to and read from Delta tables

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Databricks-Certified-Data-Engineer-Associate Schulungsunterlagen, Databricks-Certified-Data-Engineer-Associate PDF

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Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q115-Q120):

115. Frage
Which of the following is stored in the Databricks customer's cloud account?

A. Databricks web application
B. Repos
C. Notebooks
D. Data
E. Cluster management metadata

Antwort: D

Begründung:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure. Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
:
Databricks Architecture
Databricks Data Sources
Databricks Repos
[Databricks Notebooks]
[Databricks Data Engineer Professional Exam Guide]

116. Frage
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. A key-value pair configuration
B. A path to cloud storage location for the written data
C. At least one notebook library to be executed
D. A location of a target database for the written data
E. The preferred DBU/hour cost

Antwort: C

Begründung:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode. Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.

117. Frage
A company is collaborating with a partner that does not use Databricks but needs access to a large historical dataset stored in Delta format. The data engineer needs to ensure that the partner can access the data securely, without the need for them to set up an account, and with read-only access. How should the data be shared?

A. Grant your partner access to your Databricks workspace and assign them full write permissions to the Delta table, enabling them to modify the dataset.
B. Share the dataset using Unity Catalog, ensuring that both teams have full write access to the data within the same organization.
C. Share the dataset by exporting it to a CSV file and manually transferring the file to the partner's system.
D. Share the dataset using Delta Sharing, which allows your partner to access the data using a secure, read-only URL without requiring a Databricks account, ensuring that they cannot modify the data.

Antwort: D

Begründung:
Delta Sharing provides secure, read-only access to Delta tables via a sharing protocol, allowing partners without a Databricks account to query the data through a secure URL while ensuring they cannot modify it.

118. Frage
A data engineer has configured a Lakeflow Job that runs daily to ingest customer transaction data from a legacy relational database. The extracted data must be written directly to a Unity Catalog table and be immediately queryable through SQL. The team must also preserve data lineage.
Which action enables this ingestion with direct landing in Unity Catalog, preserved lineage, and immediate SQL query capability?

A. Stage the data in cloud storage through a REST API and load it with a separate job
B. Use spark.read.format("jdbc") and write the resulting DataFrame with saveAsTable("catalog.schema.table")
C. Use ODBC pooling to extract all records in one batch and write them to /dbfs/
D. Extract the data to a Pandas DataFrame, convert it to Spark, and use save() to write it to an external path

Antwort: B

119. Frage
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
A data engineer has created an ETL pipeline using Delta Live table to manage their company travel reimbursement detail, they want to ensure that the if the location details has not been provided by the employee, the pipeline needs to be terminated.
How can the scenario be implemented?

A. CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW
B. CONSTRAINT valid_location EXPECT (location = NULL)
C. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL
D. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE

Antwort: D

Begründung:
In the given scenario, the data engineer wants to ensure that if the location details have not been provided by the employee, the ETL pipeline should be terminated. This means that the data engineer needs a strict enforcement of the constraint, and the pipeline should fail if there is a violation. The correct option for this is:
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE: This will ensure that if the location is NULL, the pipeline will fail the update, effectively terminating the process as required.
The other options are incorrect because:
CONSTRAINT valid_location EXPECT (location = NULL): This is incorrect because it expects the value to be NULL, which is the opposite of what is intended here.
CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW): This would drop rows that violate the constraint, but it would not terminate the pipeline, which does not meet the requirement.
CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL: This would terminate the pipeline, but the correct syntax is ON VIOLATION FAIL UPDATE, which clearly indicates how to handle updates.

120. Frage
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