Create a sales database using the DBFS location 'dbfs:/mnt/delta/databases/sales.db/'

• A.CREATE DATABASE sales FORMAT DELTA LOCATION 'dbfs:/mnt/delta/databases/sales.db/''
• B.CREATE DATABASE sales USING LOCATION 'dbfs:/mnt/delta/databases/sales.db/'
• C.CREATE DATABASE sales LOCATION 'dbfs:/mnt/delta/databases/sales.db/'
• D.The sales database can only be created in Delta lake
• E.CREATE DELTA DATABASE sales LOCATION 'dbfs:/mnt/delta/databases/sales.db/'

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You have configured AUTO LOADER to process incoming IOT data from cloud object storage every 15 mins, recently a change was made to the notebook code to update the processing logic but the team later realized that the notebook was failing for the last 24 hours, what steps team needs to take to reprocess the data that was not loaded after the notebook was corrected?

• A.Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them
• B.Enable back_fill = TRUE to reprocess the data
• C.Delete the checkpoint folder and run the autoloader again
• D.Autoloader automatically re-processes data that was not loaded
• E.Manually re-load the data

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When investigating a data issue you realized that a process accidentally updated the table, you want to query the same table with yesterday's version of the data so you can review what the prior version looks like, what is the best way to query historical data so you can do your analysis?

• A.SELECT * FROM TIME_TRAVEL(table_name) WHERE time_stamp = 'timestamp'
• B.TIME_TRAVEL FROM table_name WHERE time_stamp = date_sub(current_date(), 1)
• C.SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
• D.DISCRIBE HISTORY table_name AS OF date_sub(current_date(), 1)
• E.SHOW HISTORY table_name AS OF date_sub(current_date(), 1)

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Which of the following data workloads will utilize a Bronze table as its destination?

• A.A job that aggregates cleaned data to create standard summary statistics
• B.A job that queries aggregated data to publish key insights into a dashboard
• C.A job that ingests raw data from a streaming source into the Lakehouse
• D.A job that develops a feature set for a machine learning application
• E.A job that enriches data by parsing its timestamps into a human-readable format

Correct Answer: C

Explanation
The answer is A job that ingests raw data from a streaming source into the Lakehouse.
The ingested data from the raw streaming data source like Kafka is first stored in the Bronze layer as first destination before it is further optimized and stored in Silver.
Medallion Architecture - Databricks
Bronze Layer:
1. Raw copy of ingested data
2. Replaces traditional data lake
3. Provides efficient storage and querying of full, unprocessed history of data
4. No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

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You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability
of this words is coming in either email could be zero. So which of the following algorithm can help you to
avoid zero probability?

• A.Naive Bayes
• B.Laplace Smoothing
• C.Logistic Regression
• D.All of the above

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