A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

• A. import dlt
  @dlt.table
  def raw_customers():
  return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
• B. import dlt
  @dlt.table
  def raw_customers():
  return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
• C. import dlt
  @dlt.table
  def raw_customers():
  return spark.read.json("s3://my-bucket/raw-customers/")
• D. import dlt
  @dlt.view
  def raw_customers():
  return spark.format.json("s3://my-bucket/raw-customers/")

A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

• A. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
• B. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
• C. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
• D. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.

To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

• A. SELECT identity_metadata.run_as AS user_email,
  SUM(usage_quantity) AS total_dbus
  FROM system.billing.usage
  GROUP BY user_email
  ORDER BY total_dbus DESC
  LIMIT 10
• B. SELECT sku_name,
  identity_metadata.created_by AS user_email,
  COUNT(usage_quantity) AS total_dbus
  FROM system.billing.usage
  GROUP BY user_email, sku_name
  ORDER BY total_dbus DESC
  LIMIT 10
• C. SELECT sku_name,
  usage_metadata.run_name AS user_email,
  SUM(usage_quantity) AS total_dbus
  FROM system.billing.usage
  GROUP BY user_email, sku_name
  ORDER BY total_dbus DESC
  LIMIT 10
• D. SELECT sku_name,
  identity_metadata.created_by AS user_email,
  SUM(usage_quantity * usage_unit) AS total_dbus
  FROM system.billing.usage
  GROUP BY user_email, sku_name
  ORDER BY total_dbus DESC
  LIMIT 10

A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

• A. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
• B. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
• C. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
• D. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.

A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

• A. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
• B. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
• C. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• D. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• E. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.