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| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and ELT Development | - ETL patterns and transformations - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark |
| Topic 2: Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Topic 3: Data Governance and Quality | - Unity Catalog basics - Data access control and governance - Data quality concepts and management |
| Topic 4: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Topic 5: Data Processing and Transformations | - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) |
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NEW QUESTION # 292
Which of the following must be specified when creating a new Delta Live Tables pipeline?
Answer: A
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.
Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.
NEW QUESTION # 293
Which of the following describes the relationship between Bronze tables and raw data?
Answer: B
Explanation:
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures "as-is", along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. Reference: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.
NEW QUESTION # 294
A data engineer is migrating pipeline tasks to reduce operational toil. The workspace uses Unity Catalog and is in a region that supports serverless. The engineer wants Databricks to auto-select instance types, manage scaling, apply Photon, and handle runtime upgrades automatically for job runs.
How should the data engineer meet this requirement while adhering to Databricks constraints?
Answer: B
Explanation:
Serverless compute for workflows is designed to minimize operational overhead for Databricks Jobs while enforcing Unity Catalog governance. When a job is configured to run on serverless compute (in supported regions), Databricks automatically selects instance types, manages autoscaling, enables Photon where applicable, and handles runtime upgrades without user intervention. This directly satisfies the requirement to reduce operational toil. All-purpose clusters (option B) and traditional job clusters (option C) still require users to manage cluster sizing, instance families, and runtime versions, which contradicts the goal of automation. SQL warehouses (option A) are intended for SQL workloads and do not support running general Python notebook job tasks as workflow steps. Databricks documentation specifies that serverless workflows are compatible with Unity Catalog and are the recommended approach when users want fully managed compute for scheduled pipelines and tasks, provided the workspace is in a supported region. This makes serverless compute the correct and fully compliant solution.
NEW QUESTION # 295
Which of the following describes the relationship between Bronze tables and raw data?
Answer: B
Explanation:
Explanation
The Bronze layer is where we land all the data from external source systems. The table structures in this layer correspond to the source system table structures "as-is," along with any additional metadata columns that capture the load date/time, process ID, etc. The focus in this layer is quick Change Data Capture and the ability to provide an historical archive of source (cold storage), data lineage, auditability, reprocessing if needed without rereading the data from the source system.https://www.databricks.com/glossary/medallion-architecture#:~:text=Bronze%20layer%20%28raw%20d
NEW QUESTION # 296
A Structured Streaming job is stopped and restarted after a code change. The engineer wants the job to resume from where it left off rather than reprocessing all source data.
Which configuration is required?
Answer: C
Explanation:
The checkpoint location stores stream progress and offset information, allowing the query to resume exactly where it stopped after a restart or failure. Each streaming query must have its own dedicated checkpoint directory; reusing or deleting it causes reprocessing or incorrect results.
NEW QUESTION # 297
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