Free DEA-C01 Exam Dumps

A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

Correct Answer:A
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.

Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)

Correct Answer:BE
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.

A large table with 200 columns contains two years of historical data. When queried. the table is filtered on a single day Below is the Query Profile:

Using a size 2XL virtual warehouse, this query look over an hour to complete What will improve the query performance the MOST?

Correct Answer:D
Adding a date column as a cluster key on the table will improve the query performance by reducing the number of micro-partitions that need to be scanned. Since the table is filtered on a single day, clustering by date will make the query more selective and efficient.

Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).

Correct Answer:BC
The functions that will compute a ‘fingerprint’ over an entire table, query result, or window to quickly detect changes to table contents or query results are:
✑ HASH_AGG(*): This function computes a hash value over all columns and rows in
a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
✑ HASH_AGG(, ): This function computes a hash value over two
expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
✑ HASH (*): This function computes a hash value over all columns in a single row.
The function returns one value per row, not one value per table, query result, or window.
✑ HASH_AGG_COMPARE (): This function compares two hash values computed by
HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
✑ HASH COMPARE(): This function compares two hash values computed by
HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.

A company has an extensive script in Scala that transforms data by leveraging DataFrames. A Data engineer needs to move these transformations to Snowpark.
…characteristics of data transformations in Snowpark should be considered to meet this requirement? (Select TWO)

Correct Answer:AB
The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:
✑ It is possible to join multiple tables using DataFrames.
✑ Snowpark operations are executed lazily on the server.
These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.
The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.