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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: Data Governance and Quality | - Unity Catalog basics - Data quality concepts and management - Data access control and governance |
| Topic 3: Productionizing Data Pipelines | - Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration |
| Topic 4: Data Processing and Transformations | - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) |
| Topic 5: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
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NEW QUESTION # 44
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
Answer: C
Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
References: The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.
NEW QUESTION # 45
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
Answer: B
Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables
NEW QUESTION # 46
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean.
However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?
Answer: E
NEW QUESTION # 47
A data engineering team needs to incrementally ingest customer transactions from a SaaS application into the Databricks Data Intelligence Platform with the following capabilities:
Built-in change data capture, including updates and deletes
Automatic schema evolution
Serverless execution with retries and minimal maintenance
OAuth support and basic monitoring
Which solution meets all the requirements?
Answer: D
Explanation:
Option D satisfies the requirements because a Lakeflow Connect managed SaaS connector provides source-specific authentication, incremental ingestion, schema evolution, and automated retries as managed capabilities. Managed connectors use serverless infrastructure and publish governed destination tables that can be processed downstream with Lakeflow Spark Declarative Pipelines. This avoids custom code for OAuth token handling, API pagination, CDC state management, retry behavior, and schema-change recovery. Option A still requires the engineering team to implement and maintain CDC and operational logic. Options B and C place most authentication, schema evolution, change tracking, and failure recovery responsibilities in custom notebooks or jobs, conflicting with the minimal-maintenance requirement. Therefore, the fully managed connector described in option D is the only solution that delivers the requested ingestion and operational behavior as an integrated platform capability.
NEW QUESTION # 48
A data engineer is working on a personal laptop and needs to perform complex transformations on data stored in a Delta Lake on cloud storage. The engineer decides to use Databricks Connect to interact with Databricks clusters and work in their local IDE. How does Databricks Connect enable the engineer to develop, test, and debug code seamlessly on their local machine while interacting with Databricks clusters?
Answer: D
Explanation:
Databricks Connect lets engineers use their preferred local IDE while connecting to Databricks clusters remotely. It mimics the Databricks runtime locally, enabling seamless development, testing, and debugging while executing workloads on the Databricks cluster.
NEW QUESTION # 49
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