The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

• A.Model, predict (df, columns)
• B.Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
• C.Df. Select (''customer_id''.
  Model (''columns) alias (''predictions'')
• D.Df.apply(model, columns). Select (''customer_id, prediction''

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The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

• A.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
• B.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
• C.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
• D.Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
• E.Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

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The data engineering team is using a SQL query to review data completeness every day to monitor the ETL job, and query output is being used in multiple dashboards which of the following ap-proaches can be used to set up a schedule and automate this process?

• A.They can schedule the query to run every day from the Jobs UI.
• B.They can schedule the query to refresh every day from the query's page in Databricks SQL
• C.They can schedule the query to run every 12 hours from the Jobs UI.
• D.They can schedule the query to refresh every day from the SQL endpoint's page in Databricks SQL.
• E.They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL

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A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

• A.Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
• B.Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
• C.Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
• D.Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

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Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

• A.All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
  (Correct)
• B.All of the data and log are stored in a single parquet file
• C.All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.
• D.All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.
• E.All of the data is stored into one parquet file, log files are broken down into one or many json files.

Correct Answer: A

Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,

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