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| Section | Objectives |
|---|---|
| Topic 1: Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - PySpark DataFrame transformations |
| Topic 2: Data Governance and Quality | - Data access control and governance - Unity Catalog basics - Data quality concepts and management |
| Topic 3: Productionizing Data Pipelines | - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs |
| Topic 4: Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
| Topic 5: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
>> Databricks-Certified-Data-Engineer-Associate Real Exam Questions <<
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NEW QUESTION # 159
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: C
NEW QUESTION # 160
Which of the following must be specified when creating a new Delta Live Tables pipeline?
Answer: D
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 # 161
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
Answer: A
Explanation:
Explanation
In the classic Databricks architecture, the control plane includes components like the Databricks web application, the Databricks REST API, and the Databricks Workspace. These components are responsible for managing and controlling the Databricks environment, including cluster provisioning, notebook management, access control, and job scheduling. The other options, such as worker nodes, JDBC data sources, Databricks Filesystem (DBFS), and driver nodes, are typically part of the data plane or the execution environment, which is separate from the control plane. Worker nodes are responsible for executing tasks and computations, JDBC data sources are used to connect to external databases, DBFS is a distributed file system for data storage, and driver nodes are responsible for coordinating the execution of Spark jobs.
NEW QUESTION # 162
Which of the following describes the type of workloads that are always compatible with Auto Loader?
Answer: C
Explanation:
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables
NEW QUESTION # 163
An organization is building a data lakehouse and needs to ingest data from multiple sources into Unity Catalog-managed tables:
Salesforce: More than 50 objects, frequent schema changes, and OAuth authentication An on-premises SQL Server database: More than 100 tables, CDC enabled, and private network connectivity required Daily JSON files landing in Azure Data Lake Storage Gen2 The organization wants all ingested data governed by Unity Catalog, minimal engineering effort for schema changes, and serverless processing wherever possible.
Which ingestion strategy meets these requirements?
Answer: C
NEW QUESTION # 164
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