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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Data transformation and modeling
|
| Topic 2: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Topic 4: Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
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NEW QUESTION # 14
Which layer contains cleaned and conformed data in Databricks Lakehouse architecture?
Answer: D
Explanation:
Silver layer contains cleaned, validated, and enriched data ready for analytics. Bronze stores raw ingested data. Gold contains aggregated business-level data. Raw is not part of formal medallion architecture naming.
NEW QUESTION # 15
Hotspot Question
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out-of-memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 16
You need to recommend a compute type for the production ingestion workloads and BI workloads. The solution must meet the environment and compute requirements.
What should you recommend for each type of workload? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Production ingestion: Job compute
BI: Serverless SQL warehouse
Job compute is designed for automated production workloads executed through Lakeflow Jobs. Its lifecycle can be tied to the job run, providing workload isolation and avoiding the cost of maintaining an interactive all- purpose cluster continuously. It is therefore appropriate for scheduled ingestion and transformation processing. A serverless SQL warehouse is designed for BI and Databricks SQL workloads. It provides rapid startup, automatic infrastructure management, scaling, and optimized SQL-query execution for dashboards and reporting tools. All-purpose compute is intended primarily for interactive notebook development and exploration, while shared compute does not provide the same job-specific isolation or SQL-serving experience. Consequently, job compute should support production ingestion, and a serverless SQL warehouse should serve the BI workload.
NEW QUESTION # 17
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You need to configure Cluster1 to meet the following requirements:
- The cluster must scale up automatically when workloads increase.
- The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
Enabling autoscaling and setting a 30-minute auto-termination timeout is the correct configuration to meet the goals.
Autoscaling handles fluctuating workloads, while auto-termination prevents you from paying for idle compute resources.
Dynamic Scaling: Autoscaling automatically adds workers during high loads and removes them when demand drops.
Cost Control: The 30-minute termination window ensures the cluster shuts down completely if no jobs are running, stopping all compute charges.
Reference:
https://community.databricks.com/t5/get-started-discussions/cluster-auto-termination-best- practices/td-p/75826
NEW QUESTION # 18
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
You need to create a new schema named schema2 that meets the following requirements:
- Is contained in catalog1
- Uses abfss://container@storageaccount.dfs.core.windows.net/data as
the managed location
Which SQL statement should you execute?
Answer: C
Explanation:
To create a new schema with a specific managed location in Azure Databricks Unity Catalog, use the CREATE SCHEMA command.
CREATE SCHEMA catalog_name.schema_name
MANAGED LOCATION 'abfss://container@storageaccount.dfs.core.windows.net/data'; Catalog Name: Replace catalog_name with your existing catalog.
Schema Name: Replace schema_name with your desired new schema name.Quotes: The storage path must be enclosed in single quotes.
Privileges: You must have CREATE SCHEMA privileges on the parent catalog and ownership (or adequate permissions) on the external location.
Reference:
https://dev.to/encorepartners/creating-your-first-catalog-schema-and-tables-in-databricks-20p3
NEW QUESTION # 19
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