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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools |
| Topic 2: Data Governance | 7% | - Unity Catalog management - Policy enforcement - Data lineage and metadata tracking |
| Topic 3: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion |
| Topic 4: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Topic 5: Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Topic 6: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Topic 7: Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Topic 8: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Topic 9: Monitoring and Alerting | 10% | - Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging |
| Topic 10: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
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NEW QUESTION # 161
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team.
The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?
Answer: A
Explanation:
Delta Lake introducedDelta Universal Format (Delta UniForm), which allowsseamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can beconverted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
* (A) Require the analytics team to use a tool that supports Delta table
* Incorrect:While Delta Lake is widely used, requiring the team to change toolsis not a flexible or scalable solution.
* (B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table
* Incorrect:
* The uniform featuremust be enabled after conversion.
* You cannotdirectly enable uniformwithout firstconverting the table.
* (C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team
* Incorrect:
* Creating a separate Iceberg copy wouldduplicate storage and increase maintenance complexity.
* This is not necessary whenDelta UniForm allows direct compatibility with Iceberg.
* (D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table
* Correct:
* The best approach is toconvert the existing Delta table to Icebergusing the Databricks Delta to Iceberg migration tools.
* After conversion,enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice forinteroperability between Delta and Icebergis toconvert the Delta table to Iceberg and enable uniform, ensuringcross-compatibilitywithout data duplication.Thus,Option (D) is the correct answer.
References:
* Delta UniForm for Apache Iceberg - Databricks Documentation
* Convert Delta to Iceberg - Databricks
NEW QUESTION # 162
The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode.
what is the expected outcome after clicking Start to update the pipeline?
Answer: E
Explanation:
Explanation
The answer is All datasets will be updated once and the pipeline will shut down. The compute re-sources will persist to allow for additional testing.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default, pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Please review additional DLT concepts using below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c
NEW QUESTION # 163
Data engineering team is required to share the data with Data science team and both the teams are using different workspaces in the same organizationwhich of the following techniques can be used to simplify sharing data across?
*Please note the question is asking how data is shared within an organization across multiple workspaces.
Answer: A
Explanation:
Explanation
The answer is the Unity catalog.
Diagram Description automatically generated
Unity Catalog works at the Account level, it has the ability to create a meta store and attach that meta store to many workspaces see the below diagram to understand how Unity Catalog Works, as you can see a metastore can now be shared with both workspaces using Unity Catalog, prior to Unity Catalog the options was to use single cloud object storage manually mount in the second databricks workspace, and you can see here Unity Catalog really simplifies that.
Diagram Description automatically generated with medium confidence
sorry for the inconvenience watermark was added because other people on Udemy are copying my questions and images.
duct features
https://databricks.com/product/unity-catalog
NEW QUESTION # 164
A junior data engineer on your team has implemented the following code block.
The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?
Answer: A
Explanation:
Explanation
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged."https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20o
NEW QUESTION # 165
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?
Answer: B
Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake toensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
* transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes
* Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html
NEW QUESTION # 166
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