Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps Questions & Answers

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON .
The code snippet is:
df = (spark.readStream
.format( " cloudFiles " )
.option( " cloudFiles.format " , " json " )
.option( " badRecordsPath " , " /tmp/somewhere/badRecordsPath " )
.schema( " a int, b int " )
.load( " /Volumes/catalog/schema/raw_data/ " ))
What is the cause of the missing data?

• A.The badRecordsPath location is accumulating many small files.
• B.The source data is valid JSON but does not conform to the defined schema in some way.
• C.The engineer forgot to set the option " cloudFiles.quarantineMode " = " rescue " .
• D.At some point, the upstream data provider switched everything to multi-line JSON.

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?

• A.Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
• B.Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• C.Use the "Validate" option in the notebook to check for syntax errors.
• D.Open the web terminal from the notebook and run a shell command to validate the pipeline code.

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format( " parquet " ).load(f " /mnt/source/(date) " )
Which code block should be used to create the date Python variable used in the above code block?

• A.dbutils.widgets.text( " date " , " null " )
  date = dbutils.widgets.get( " date " )
• B.input_dict = input()
  date= input_dict[ " date " ]
• C.date = spark.conf.get( " date " )
• D.import sys
  date = sys.argv[1]
• E.date = dbutils.notebooks.getParam( " date " )

A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events . Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region.
The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve.

• A.Enable Delta caching.
• B.Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
• C.Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
• D.Enable auto-compaction (optimizeWrite and autoCompact).

A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

• A.A high-concurrency cluster designed for interactive SQL workloads.
• B.A lightweight single-node cluster with low worker node count to reduce costs.
• C.A job cluster configured to autoscale across multiple workers during the pipeline run.
• D.An all-purpose cluster always kept running to ensure low-latency job startup times.