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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling and Storage | 20% | - Data Modeling - Storage Optimization - File Formats |
| Topic 2: Data Quality and Governance | 12% | - Data Lineage - Data Quality - Governance |
| Topic 3: Monitoring and Troubleshooting | 16% | - Performance Optimization - Monitoring - Troubleshooting |
| Topic 4: Databricks Lakehouse Platform | 24% | - Delta Lake - Lakehouse Architecture - Unity Catalog - Data Management |
| Topic 5: Data Processing | 28% | - ETL Pipelines - Data Transformation - Spark SQL - Structured Streaming |
1. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)
A) Create init scripts on DBFS.
B) Use compute policies to set system properties, environment variables, and Spark configuration parameters.
C) Add libraries to compute policies
D) Install libraries on DBFS
E) Use secrets in init scripts to store configuration data
2. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?
A) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
D) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
3. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?
A) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
B) Dynamic data masking is applied only at rest, so it does not affect query performance.
C) PII masking is only required for direct identifiers.
D) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
4. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
A) Only workspace administrators can grant "Owner" privileges to a group.
B) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C) Other than the default "admins" group, only individual users can be granted privileges on jobs.
D) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E) A user can only transfer job ownership to a group if they are also a member of that group.
5. The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?
A) When the workspace is being configured, make sure that external cloud object storage has been mounted.
B) Whenever a database is being created, make sure that the location keyword is used
C) Whenever a table is being created, make sure that the location keyword is used.
D) When tables are created, make sure that the external keyword is used in the create table statement.
E) When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: C |
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