A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

• A.databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster
• B.databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• C.databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• D.databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster

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What is the first of a Databricks Python notebook when viewed in a text editor?

• A.%python
• B.# Databricks notebook source
• C.-- Databricks notebook source
• D.// Databricks notebook source
• E.# MAGIC %python

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A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

• A.@dlt.table(name="trips_data_quarantine")
  def trips_data_quarantine():
  return (
  spark.readStream.table("raw_trips_data")
  .filter(expr(quarantine_rules))
  )
• B.@dlt.view
  @dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
  return spark.readStream.table("ride_and_go.telemetry.trips")
• C.@dlt.table
  @dlt.expect_all_or_drop(rules)
  def trips_data_quarantine():
  return spark.readStream.table("raw_trips_data")
• D.@dlt.table(partition_cols=["is_quarantined", ])
  @dlt.expect_all(rules)
  def trips_data_quarantine():
  return (
  spark.readStream.table("raw_trips_data")
  .withColumn("is_quarantined", expr(quarantine_rules))
  )

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

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The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

• A.No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
• B.Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
• C.No; the change data feed only tracks inserts and updates not deleted records.
• D.No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
• E.Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.

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