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| Section | Objectives |
|---|---|
| Topic 1: Productionizing Data Pipelines | - Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration |
| Topic 2: Data Governance and Quality | - Unity Catalog basics - Data quality concepts and management - Data access control and governance |
| Topic 3: Data Ingestion and ELT Development | - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data |
| 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 | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
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NEW QUESTION # 84
A team creates YAML manifests that declare jobs, resources, and dependencies, then deploys them to Databricks using the Databricks CLI. The deployment succeeds.
Which feature are they using?
Answer: B
Explanation:
Databricks Asset Bundles are a Databricks CLI feature that lets teams define Databricks projects "as code" using YAML configuration files. In a bundle, metadata is expressed in YAML (for example, a required databricks.yml at the root), and the configuration can declare resources (such as Databricks Jobs/workflows and other supported assets), artifacts, and deployment targets. After defining these YAML manifests, teams use the Databricks CLI to validate, deploy, and run the bundle into a Databricks workspace, which matches the scenario described (YAML + CLI deployment). This approach standardizes how workflows and related resources are packaged and promoted across environments (dev/test/prod) while keeping definitions version-controlled and reproducible. GitHub is only a hosting/version-control platform (not the YAML deployment feature itself), Terraform is a separate IaC tool, and "DataOps" is a methodology rather than a specific Databricks feature. (Databricks Documentation)
NEW QUESTION # 85
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: C
Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher- Order Functions.
NEW QUESTION # 86
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?
Answer: D
NEW QUESTION # 87
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository.
A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?
Answer: B
NEW QUESTION # 88
A data engineer wants to run SQL queries directly on data stored in Delta Lake using a serverless compute environment designed for analytics workloads. Which Databricks component provides this capability?
Answer: A
NEW QUESTION # 89
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