https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.D

ataStreamWriter.trigger.html

The processingTime option specifies a time-based trigger interval for fixed interval micro-batches.

This means that the query will execute a micro-batch to process data every 5 seconds, regardless of

how much data is available. This option is suitable for near-real time processing workloads that

require low latency and consistent processing frequency. The other options are either invalid syntax

(A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation -

Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

The DELETE command in Delta Lake allows you to remove data that matches a predicate from a

Delta table. This command will delete all the rows where the value in the column age is greater than

25 from the existing Delta table my_table and save the updated table. The other options are either

incorrect or do not achieve the desired result. Option A will only select the rows that match the

predicate, but not delete them. Option B will update the rows that match the predicate, but not

delete them. Option D will update the rows that do not match the predicate, but not delete them.

Option E will delete the rows that do not match the predicate, which is the opposite of what we

want. Reference: Table deletes, updates, and merges — Delta Lake Documentation

Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out

employees with more than 5 years of experience from the array column “employees”. It applies a

lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the

array is greater than 5. If this condition is met, the employee is included in the new array column

“exp_employees”.

Reference: The use of higher-order functions like FILTER can be referenced from Databricks

documentation on Higher-Order Functions.

Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data

being processed in each trigger. This ensures that the engine can reliably track the exact progress of

the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the

mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it

can be recovered after a failure. Write-ahead logs are files that record the offset range of the data

being processed in each trigger and are written to the checkpoint location before the processing

starts. These logs are used to recover the query state and resume processing from the last processed

offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault

Tolerance Semantics

This approach would allow the data engineering team to use the existing SQL program and add some

logic to control the execution of the final query based on the day of the week. They could use

the datetime module in Python to get the current date and check if it is a Sunday. If so, they could

run the final query, otherwise they could skip it. This way, they could schedule the program to run

every day without changing the data model or the source table. Reference: PySpark SQL

Module, Python datetime Module, Databricks Jobs

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency,

and scale of data lakes with the data management and ACID transactions of data warehouses,

enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data

lakehouse, both the data analysis and data engineering teams can access the same data sources and

formats, ensuring data consistency and quality across their reports. A data lakehouse also supports

schema enforcement and evolution, data validation, and time travel to old table versions, which can

help resolve data conflicts and errors1. Reference: 1: What is a Data Lakehouse? - Databricks 2: What

is a data lakehouse? | IBM

Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data

processing pipelines. You define the transformations to perform on your data and Delta Live Tables

manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta

Live Tables supports both SQL and Python as the languages for defining your datasets and

expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both

append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse

architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to

Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data

engineer and data analyst can use the same languages, sources, and architecture as before, and

simply declare their datasets and expectations using Delta Live Tables syntax. Reference:

What is Delta Live Tables?

Transform data with Delta Live Tables

What is the medallion lakehouse architecture?

To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON

TABLE statement. This statement will grant all the possible privileges on the table, such

as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax

correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a

subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not

enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the

table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on

the table team to the user or group sales, which is the opposite of what the question

asks. Reference: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a

table using SQL - Azure Databricks, SQL Privileges - Databricks

The query performing a streaming hop from raw data to a Bronze table is identified by using the

Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:

Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of

a roll-up into a summarized table rather than a hop into a Bronze table.

Option B: Also uses .writeStream but calculates an average, which again does not typically represent

the raw to Bronze transformation, which usually involves minimal transformations.

Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and

hence not suitable for real-time streaming data transformation to a Bronze table.

Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out

with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is

captured in real-time and minimal transformation is applied. This approach aligns with the concept

of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored

in a more accessible format.

Reference:

Databricks documentation on Structured Streaming: Structured Streaming in Databricks