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| Section | Objectives |
|---|---|
| Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
| Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Data Governance and Quality | - Data access control and governance - Data quality concepts and management - Unity Catalog basics |
| Data Processing and Transformations | - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations |
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NEW QUESTION # 117
Which Databricks SQL predicate correctly performs a null-safe equality comparison so that rows are matched when both sides are NULL or when both are equal non-NULL values?
Answer: B
Explanation:
Databricks SQL uses the null-safe equality operator <=>. It returns TRUE when both values are NULL or when both non-NULL values are equal. The operator must be written without spaces:
customer_id <=> :cid.
NEW QUESTION # 118
A data engineer needs to access the view created by the sales team, using a shared cluster. The data engineer has been provided usage permissions on the catalog and schema. In order to access the view created by sales team. What are the minimum permissions the data engineer would require in addition?
Answer: D
NEW QUESTION # 119
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?
Answer: A
NEW QUESTION # 120
A developer is building a data pipeline that processes records from a bronze table into a silver table. The bronze table, bronze_events, contains duplicate records due to at-least-once delivery guarantees from the upstream ingestion system. The developer writes the following PySpark code to prepare the data:
The developer notices that after running dropDuplicates() without arguments, some rows that differ only in the event_timestamp column remain. The developer wants to de-duplicate based only on user_id and event_type, keeping one row per unique combination of these two columns.
Which code change achieves this deduplication requirement?
Answer: D
Explanation:
dropDuplicates(["user_id", "event_type"]) removes rows based only on those two columns while retaining one complete row for each unique combination.
NEW QUESTION # 121
An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.
Which approach fulfills these requirements?
Answer: C
Explanation:
Databricks documentation recommends serverless compute as the simplest and most reliable compute option when the workload is supported. Serverless compute is designed to automatically provision resources, scale with demand, reduce infrastructure management, and apply platform optimizations without requiring users to configure clusters manually. This directly supports the requirement for automatic scaling, reduced operational overhead, and better alignment with strict SLAs. Databricks also states that serverless compute is always available and scales according to workload, making it a strong fit for organizations seeking high availability and minimal downtime. Fixed-configuration job clusters in option B still require manual sizing decisions and do not meet the "no manual cluster management" requirement. Spot instances in option C may reduce costs but can be interrupted, which makes them a poor choice when reliability is a top requirement. Interactive clusters in option D are intended more for development and exploration and still need manual management. Based on Databricks guidance, serverless compute is the correct choice.
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NEW QUESTION # 122
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