Study with Databricks-Certified-Data-Engineer-Professional Actual Test Questions

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

A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

• A. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
• B. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
• C. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
• D. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.

A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

• A. Set the skipChangeCommits flag to true on bpm_stats
• B. Set the SkipChangeCommits flag to true raw_lot
• C. Set the pipelines, reset, allowed property to false on bpm_stats
• D. Set the pipelines, reset, allowed property to false on raw_iot

What statement is true regarding the retention of job run history?

• A. It is retained for 60 days, after which logs are archived
• B. It is retained for 90 days or until the run-id is re-used through custom run configuration
• C. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
• D. It is retained until you export or delete job run logs
• E. It is retained for 60 days, during which you can export notebook run results to HTML

A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

• A. partitionBy
• B. overwriteSchema
• C. mergeSchema
• D. checkpointLocation