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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion | 15-20% | - Batch ingestion methods
|
| Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Delta Lake | 20-25% | - Delta Lake fundamentals
|
| Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
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NEW QUESTION # 142
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.
Answer: C
Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
Reference:
Databricks documentation on using Repos with Git: https://docs.databricks.com/repos.html
NEW QUESTION # 143
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected.
Which command will trigger the job execution?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.
Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.
The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.
Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn't integrate with bundle targets. Therefore, D is the correct and verified answer.
NEW QUESTION # 144
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.
Which solution would improve the performance?
Answer: C
Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime , which is suitable for correlating events that occur at the exact same time.
However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It ' s important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that ' s unlikely to match with new data.
NEW QUESTION # 145
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table") \
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Delta Lake's data skipping and file pruning optimizations rely on metadata about columns used in partitioning or Z-ordering. If a filter column (e.g., event_date) is not included in the partition or Z-ordering keys, Spark cannot effectively prune files at query time, resulting in full table scans. The Databricks optimization guide states that "File pruning and data skipping are most effective when queries filter on partition or Z-order columns." This explains why the filter was applied but had no impact on the amount of data read. Options A and B are incorrect because Delta automatically applies file pruning when possible; D is less likely, as date columns are fully supported for skipping.
NEW QUESTION # 146
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance.
How should the data engineer create the patient_encounters table?
Answer: D
Explanation:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog. Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.
NEW QUESTION # 147
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