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| Section | Objectives |
|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
|
| Topic 2: Data Governance | - Metadata and Discoverability
|
| Topic 3: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 4: Data Transformation, Cleansing, and Quality | - Data Quality
|
| Topic 5: Ensuring Data Security and Compliance | - Compliance
|
| Topic 6: Monitoring and Alerting | - Alerting
|
| Topic 7: Debugging and Deploying | - Deploying CI/CD
|
| Topic 8: Data Modelling | - Dimensional Modelling
|
| Topic 9: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 10: Cost & Performance Optimisation | - Delta Optimization
|
1. A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)
A) Use ZORDER on high-cardinality columns.
B) Apply liquid clustering using the merge join keys.
C) Partition the table by date.
D) Overwrite the table instead of Merge.
E) Enable deletion vectors on the table if not already enabled.
2. All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?
A) Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
B) Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
C) Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
D) Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
E) All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
3. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?
A)
B)
C)
D) 
4. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
A) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
B) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
C) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
D) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
E) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
5. When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?
A) The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
B) The group needs to have USE CATALOG and USE SCHEMA on the catalog.
C) The group needs to have ALL PRIVILEGES on the catalog.
D) The group should be made OWNER of the catalog.
Solutions:
| Question # 1 Answer: B,E | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: B |
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