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| Section | Objectives |
|---|---|
| Topic 1: Production Pipelines and Orchestration | - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling |
| Topic 2: Security, Governance, Monitoring, and Optimization | - Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads - Cost optimization and performance tuning |
| Topic 3: Data Ingestion and Transformation | - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks |
| Topic 4: Data Modeling and Storage | - Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies |
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NEW QUESTION # 142
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
Which statement describes this implementation?
Answer: B
Explanation:
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.
NEW QUESTION # 143
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources.
Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?
Answer: C
Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
:
Delta Lake Documentation on Time Travel: Delta Time Travel
Delta Lake Versioning: Delta Lake Versioning Guide
NEW QUESTION # 144
Which statement describes integration testing?
Answer: C
Explanation:
Explanation
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.
NEW QUESTION # 145
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data.
Which statement is applicable for Lakehouse Federations to maintain data consistency?
Answer: A
Explanation:
Lakehouse Federation allows Databricks to query and manage external data sources through a single governance layer, without moving or copying data . The documentation specifies that "Federated queries provide read-only access to data, reflecting the current state of the underlying source system." This ensures consistency across teams since all users access the same source of truth directly from the external system through Unity Catalog. Federation does not perform CDC replication or local caching; it queries live data on demand. Hence, option A accurately represents how Lakehouse Federation maintains consistency across federated sources.
NEW QUESTION # 146
Which of the following statement is true about Databricks repos?
Answer: C
Explanation:
Explanation
See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops Diagram Description automatically generated
NEW QUESTION # 147
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