Databricks DATABRICKS Practice Questions

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Question 71

What is the purpose of the bronze layer in a Multi-hop Medallion architecture?

Copy of raw data, easy to query and ingest data for downstream processes.

Powers ML applications

Data quality checks, corrupt data quarantined

Contain aggregated data that is to be consumed into Silver

Reduces data storage by compressing the data

Question 72

What is the purpose of the silver layer in a Multi hop architecture?

Replaces a traditional data lake

Efficient storage and querying of full, unprocessed history of data

Eliminates duplicate data, quarantines bad data

Refined views with aggregated data

Optimized query performance for business-critical data

Question 73

What is the purpose of gold layer in Multi hop architecture?

Optimizes ETL throughput and analytic query performance

Eliminate duplicate records

Preserves grain of original data, without any aggregations

Data quality checks and schema enforcement

Optimized query performance for business-critical data

Question 74

The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode. what is the expected outcome after clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional development and testing

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional development and testing

All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline

Question 75

The Delta Live Table Pipeline is configured to run in Production mode using the continuous Pipeline Mode. what is the expected outcome after clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional testing

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline

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Correct AnswerE

The answer is,

All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline until it is shut down since the execution mode is chosen to be continuous. It does not matter if the pipeline mode is development or production, pipeline mode only matters during the pipeline initialization.

DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.

Development and production modes

Development:

When you run your pipeline in development mode, the Delta Live Tables system:

Reuses a cluster to avoid the overhead of restarts.
Disables pipeline retries so you can immediately detect and fix errors.

Production:

In production mode, the Delta Live Tables system:

Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.

Use the buttons in the Pipelines UI to switch between development and production modes. By default, pipelines run in development mode.

Switching between development and production modes only controls cluster and pipeline execution behavior. Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.

Delta Live Tables supports two different modes of execution:

Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipeline are updated after their dependent data sources have been updated.

Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data

Please review additional DLT concepts using the below link

https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-concepts

Question 76

You are working to set up two notebooks to run on a schedule, the second notebook is dependent on the first notebook but both notebooks need different types of compute to run in an optimal fashion, what is the best way to set up these notebooks as jobs?

Use DELTA LIVE PIPELINES instead of notebook tasks

A Job can only use single cluster, setup job for each notebook and use job dependency to link both jobs together

Each task can use different cluster, add these two notebooks as two tasks in a single job with linear dependency and modify the cluster as needed for each of the tasks

Use a single job to setup both notebooks as individual tasks, but use the cluster API to setup the second cluster before the start of second task

Use a very large cluster to run both the tasks in a single job

Question 77

You are tasked to set up a set notebook as a job for six departments and each department can run the task parallelly, the notebook takes an input parameter dept number to process the data by department, how do you go about to setup this up in job?

Use a single notebook as task in the job and use dbutils.notebook.run to run each notebook with parameter in a different cell

A task in the job cannot take an input parameter, create six notebooks with hardcoded dept number and setup six tasks with linear dependency in the job

A task accepts key-value pair parameters, creates six tasks pass department number as parameter foreach task with no dependency in the job as they can all run in parallel.

A parameter can only be passed at the job level, create six jobs pass department number to each job with linear job dependency

A parameter can only be passed at the job level, create six jobs pass department number to each job with no job dependency

Question 78

You are asked to setup two tasks in a databricks job, the first task runs a notebook to download the data from a remote system, and the second task is a DLT pipeline that can process this data, how do you plan to configure this in Jobs UI

Single job cannot have a notebook task and DLT Pipeline task, use two different jobs with linear dependency.

Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in continuous mode.

Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in trigger mode.

Single job can be used to setup both notebook and DLT pipeline, use two different tasks with linear dependency.

Add first step in the DLT pipeline and run the DLT pipeline as triggered mode in JOBS UI

Question 79

You are asked to set up an alert to notify in an email every time a KPI indicater increases beyond a threshold value, team also asked you to include the actual value in the alert email notification.

Use notebook and python code to run every minute, using python variables to capture send the information in an email

Setup an alert but use the default template to notify the message in email’s subject

Setup an alert but use the custom template to notify the message in email’s subject

Use the webhook destination instead so alert message can be customized

Use custom email hook to customize the message

Question 80

Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

Use notebook and Jobs to use python to publish DQ metrics

Setup an alert to send an email, use python to parse email, and publish a webhook message

Setup an alert with custom template

Setup an alert with custom Webhook destination

Setup an alert with dynamic template

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