A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schem a. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

• A.No; Delta Lake manages streaming checkpoints in the transaction log.
• B.Yes; both of the streams can share a single checkpoint directory.
• C.No; only one stream can write to a Delta Lake table.
• D.Yes; Delta Lake supports infinite concurrent writers.
• E.No; each of the streams needs to have its own checkpoint directory.

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You are designing an analytical to store structured data from your e-commerce platform and un-structured data from website traffic and app store, how would you approach where you store this data?

• A.Use traditional data warehouse for structured data and use data lakehouse for un-structured data.
• B.Data lakehouse can only store unstructured data but cannot enforce a schema
• C.Data lakehouse can store structured and unstructured data and can enforce schema
• D.Traditional data warehouses are good for storing structured data and enforcing schema

Correct Answer: C

Explanation
The answer is, Data lakehouse can store structured and unstructured data and can enforce schema What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated

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What is the main difference between the below two commands?
1.INSERT OVERWRITE table_name
2.SELECT * FROM table
1.CREATE OR REPLACE TABLE table_name
2.AS SELECT * FROM table

• A.INSERT OVERWRITE replaces data by default, CREATE OR REPLACE replaces data and Schema by default
• B.INSERT OVERWRITE replaces data and schema by default, CREATE OR REPLACEreplaces data by default
• C.INSERT OVERWRITE maintains historical data versions by de-fault, CREATE OR REPLACEclears the historical data versions by default
• D.INSERT OVERWRITE clears historical data versions by de-fault, CREATE OR REPLACE maintains the historical data versions by default
• E.Both are same and results in identical outcomes

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Which of the following python statement can be used to replace the schema name and table name in the query statement?

• A.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from schema_name.table_name"
• B.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = "select * from {schema_name}.{table_name}"
• C.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from { schema_name}.{table_name}"
• D.1.table_name = "sales"
  2.schema_name = "bronze"
  3.query = f"select * from + schema_name +"."+table_name"

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A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?

• A.They could replace the string-formatted date in the table with a timestamp-formatted date
• B.They could request that the data analyst rewrites the query to be run less frequently
• C.They could wrap the query using PySpark and use Python's string variable system to automatically
  update the table name
• D.They could pass the table into PySpark and develop a robustly tested module on the existing query
• E.They could manually replace the date within the table name with the current day's date

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