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| Section | Objectives |
|---|---|
| Topic 1: Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Topic 2: Data Ingestion and ELT Development | - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data |
| Topic 3: Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Topic 4: Data Governance and Quality | - Data quality concepts and management - Data access control and governance - Unity Catalog basics |
| Topic 5: Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations |
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NEW QUESTION # 15
Which of the following must be specified when creating a new Delta Live Tables pipeline?
Answer: B
Explanation:
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.
References: Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.
NEW QUESTION # 16
A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.
Which workspace requirement should be confirmed first to avoid connection failures?
Answer: C
Explanation:
Databricks Connect requires Unity Catalog to be enabled when connecting to serverless compute, and the Databricks Connect version must explicitly support serverless for the target Databricks Runtime to ensure compatibility and prevent connection failures.
NEW QUESTION # 17
A data engineer is reviewing the documentation on audit logs in Databricks for compliance purposes and needs to understand the format in which audit logs output events.
How are events formatted in Databricks audit logs?
Answer: B
NEW QUESTION # 18
Which of the following describes a scenario in which a data engineer will want to use a single- node cluster?
Answer: A
NEW QUESTION # 19
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
Answer: C
Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. Reference: Workspace access control, Manage Unity Catalog object ownership.
NEW QUESTION # 20
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