When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

• A. In the Spark UI, under the SQL/DataFrame tab
• B. In the Query Profiler, under the Stages tab
• C. In the Spart UI, under the Jobs tab
• D. In the Query Profiler, under Query Source

說明：（僅 NewDumps 成員可見）

A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

• A. The total count of rows is calculated by scanning all data files
• B. The total count of records is calculated from the Hive metastore
• C. The total count of records is calculated from the parquet file metadata
• D. The total count of records is calculated from the Delta transaction logs
• E. The total count of rows will be returned from cached results unless REFRESH is run

說明：（僅 NewDumps 成員可見）

A data engineer inherits a Delta table with historical partitions by country that are badly skewed.
Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve. Which two actions should the data engineer take? (Choose two.)

• A. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
• B. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
• C. Periodically run OPTIMIZE table_name.
• D. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
• E. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

說明：（僅 NewDumps 成員可見）

A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

• A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
• B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
• C. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
• D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
• E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

說明：（僅 NewDumps 成員可見）

A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

• A. @dlt.table
  @dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
  def silver_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
  @dlt.table
  @dlt.expect("invalid_sensor_reading", "reading >= 120")
  def quarantine_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
• B. @dlt.table
  @dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
  def silver_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
  @dlt.table
  @dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
  def quarantine_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
• C. @dlt.table
  @dlt.expect("valid_sensor_reading", "reading < 120")
  def silver_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
  @dlt.table
  @dlt.expect("invalid_sensor_reading", "reading >= 120")
  def quarantine_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
• D. @dlt.table
  @dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
  def silver_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")
  @dlt.table
  @dlt.expect("invalid_sensor_reading", "reading < 120")
  def quarantine_sensor_readings():
  return spark.readStream.table("bronze_sensor_readings")

說明：（僅 NewDumps 成員可見）