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| Section | Objectives |
|---|---|
| Topic 1: Productionizing Data Pipelines | - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs |
| Topic 2: Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
| Topic 3: Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations |
| Topic 4: Data Governance and Quality | - Data quality concepts and management - Data access control and governance - Unity Catalog basics |
| Topic 5: Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
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NEW QUESTION # 176
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?
Answer: B
Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
Reference:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]
NEW QUESTION # 177
A data engineer is standardizing repository layouts for multiple teams adopting Databricks Asset Bundles.
The engineer wants to ensure every project has a single authoritative configuration file at the repository root that defines the bundle name, targets, workspace settings, permissions, and resource mappings (for jobs and pipelines).
Which strategy should the data engineer use to meet this goal?
Answer: C
Explanation:
In Databricks Asset Bundles , the databricks.yml file located at the repository root serves as the single authoritative configuration file for the entire project. This file defines key elements such as the bundle name, deployment targets (for example, dev, test, prod), workspace configurations, permissions, and resource definitions including jobs and pipelines. Databricks documentation specifies that there must be one primary databricks.yml file at the root , ensuring consistency and simplifying deployment processes. However, to support modularity and maintainability, this root configuration file can reference additional YAML files using the include mapping. This allows teams to organize configurations across directories while still maintaining a single source of truth. Option A is incorrect because multiple root-level configuration files are not supported as independent authorities. Option C is invalid since configuration files are not restricted to hidden directories. Option D introduces ambiguity and contradicts the requirement for a single authoritative configuration. Therefore, using one root-level configuration file with optional includes is the correct and recommended approach.
NEW QUESTION # 178
A data engineer needs to develop integration tests for an ETL process and deploy a version- controlled, packaged workflow into production using an external job scheduler. Which tool should the data engineer use for this job?
Answer: B
Explanation:
Databricks Asset Bundles enable packaging of workflows with version control, allowing integration testing and deployment into production while supporting orchestration by external job schedulers.
NEW QUESTION # 179
A data engineer working in Databricks needs to control BI query latency and cost for a sales analytics domain. Silver tables already contain validated, normalized sales and product data. The team wants dashboard-friendly query performance and to align datasets with business concepts (e.g., weekly_sales and account_performance) without overloading analysts with complex joins.
Which design approach should a data engineer choose for the Gold layer in a medallion architecture?
Answer: A
Explanation:
The Gold layer is designed for business-level consumption, so creating denormalized, domain- aligned tables and precomputed aggregates reduces query complexity and latency while optimizing cost for BI workloads and dashboards.
NEW QUESTION # 180
A data engineer is cleaning a Bronze table that receives the same customer records from multiple source systems. Duplicate rows have the same customer_id and email values but different ingestion_timestamp values. The Silver table should contain only one record for each unique combination of customer_id and email.
Which PySpark operation correctly deduplicates the records based on the business keys?
Answer: A
NEW QUESTION # 181
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