Databricks DATABRICKS Practice Questions

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Question 1

Which of the statements are incorrect when choosing between lakehouse and Datawarehouse?

Lakehouse can have special indexes and caching which are optimized for Machine learning

Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.

Lakehouse can be accessed through various API’s including but not limited to Python/R/SQL

Traditional Data warehouses have storage and compute are coupled.

Lakehouse uses standard data formats like Parquet.

Question 2

Which of the statements are correct about lakehouse?

Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads

Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads

Lakehouse does not support ACID

In Lakehouse Storage and compute are coupled

Lakehouse supports schema enforcement and evolution

Question 3

Which of the following are stored in the control pane of Databricks Architecture?

Question 4

You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

Setup an additional job to run ahead of the actual job so the cluster is running second job starts

Use the Databricks cluster pools feature to reduce the startup time

Use Databricks Premium edition instead of Databricks standard edition

Pin the cluster in the cluster UI page so it is always available to the jobs

Disable auto termination so the cluster is always running

Question 5

Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos.

Trigger Databricks Repos pull API to update the latest version

Commit and push code

Create and edit code

Create a new branch

Pull request and review process

Question 6

You have noticed the Data scientist team is using the notebook versioning feature with git integration, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

Databricks Repos allows multiple users to make changes

Databricks Repos allows merge and conflict resolution

Databricks Repos has a built-in version control system

Databricks Repos automatically saves changes

Databricks Repos allow you to add comments and select the changes you want to commit.

Question 7

Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

Setup multiple clusters so each team member has their own cluster

Disable the auto-scaling feature

Use High concurrency mode instead of the standard mode

Increase the size of the driver node

Question 8

Which of the following SQL commands are used to append rows to an existing delta table?

Question 9

How are Delt tables stored?

A Directory where parquet data files are stored, a sub directory _delta_log where meta data, and the transaction log is stored as JSON files.

A Directory where parquet data files are stored, all of the meta data is stored in memory

A Directory where parquet data files are stored in Data plane, a sub directory _delta_log where meta data, history and log is stored in control pane.

A Directory where parquet data files are stored, all of the metadata is stored in parquet files

Data is stored in Data plane and Metadata and delta log are stored in control pane

Question 10

While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?

Review event logs in the Workspace

Run SQL SHOW HISTORY table_name

Check Databricks SQL Audit logs

Run SQL command DESCRIBE HISTORY table_name

Review workspace audit logs

Update History

Update History