A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

• A. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
• B. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
• C. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
• D. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.

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 Spart Declarative Pipelines with Auto Loader and enabling schema inference with
  "cloudFiles.schemaEvolutionMode"= "addNewColumns"
• C. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
• D. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.

Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

• A. Scala Datasets
• B. C++
• C. pyspsark.ml.feature
• D. Julia
• E. Regex

Which of the following is true of Delta Lake and the Lakehouse?

• A. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
• B. Z-order can only be applied to numeric values stored in Delta Lake tables
• C. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
• D. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
• E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

• A. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
• B. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
• C. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
• D. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.