Pass the Snowflake SnowPro Advanced Certification DEA-C01 Questions and answers with CertsForce

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.

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.

The Snowflake Kafka connector uses three Snowflake objects: pipe, internal table stage, and internal named stage. The pipe object is used to load data from an external stage into a Snowflake table using COPY statements. The internal table stage is used to store files that are loaded from Kafka topics into Snowflake using PUT commands. The internal named stage is used to store files that are rejected by the COPY statements due to errors or invalid data. The other options are not objects that are used by the Snowflake Kafka connector. Option B, serverless task, is an object that can execute SQL statements on a schedule without requiring a warehouse. Option C, internal user stage, is an object that can store files for a specific user in Snowflake using PUT commands. Option F, storage integration, is an object that can enable secure access to external cloud storage services without exposing credentials.

An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.

The ways that Company A can share data with Company B are:

Create a share within Company A’s account and add Company B’s account as a recipient of that share: This is a valid way to share data between different accounts on different cloud platforms and regions. Snowflake supports cross-cloud and cross-region data sharing, which allows users to create shares and grant access to other accounts regardless of their cloud platform or region. However, this option may incur additional costs for network transfer and storage replication.

Create a separate database within Company A’s account to contain only those data sets they wish to share with Company B Create a share within Company A’saccount and add all the objects within this separate database to the share Add Company B’s account as a recipient of the share: This is also a valid way to share data between different accounts on different cloud platforms and regions. This option is similar to the previous one, except that it uses a separate database to isolate the data sets that need to be shared. This can improve security and manageability of the shared data. The other options are not valid because:

Create a share within Company A’s account, and create a reader account that is a recipient of the share Grant Company B access to the reader account: This option is not valid because reader accounts are not supported for cross-cloud or cross-region data sharing. Reader accounts are Snowflake accounts that can only consume data from shares created by their provider account. Reader accounts must be on the same cloud platform and region as their provider account.

Use database replication to replicate Company A’s data into Company B’s account Create a share within Company B’s account and grant users within Company B’s account access to the share: This option is not valid because database replication cannot be used for cross-cloud or cross-region data sharing. Database replication is a feature in Snowflake that allows users to copy databases across accounts within the same cloud platform and region. Database replication cannot copy databases across different cloud platforms or regions.

Create a new account within Company A’s organization in the same cloud provider and region as Company B’s account Use database replication to replicate Company A’s data to the new account Create a share within the new account and add Company B’s account as a recipient of that share: This option is not valid because it involves creating a new account within Company A’s organization, which may not be feasible or desirable for Company A. Moreover, this option is unnecessary, as Company A can directly share data with Company B without creating an intermediate account.

The system function SYSTEM$ABORT_TRANSACTION can be used to abort a detached transaction that was left in an open state due to a session disconnect or termination. The function takes one argument: the transaction ID of the detached transaction. The function will abort the transaction and release any locks held by it. The other options are incorrect because they do not address the issue of a detached transaction. The system function SYSTEM$CANCEL_TRANSACTION can be used to cancel a running transaction, but not a detached one. The LOCK_TIMEOUT parameter can be used to set a timeout period for acquiring locks on resources, but it does not affect existing locks. The TRANSACTION_ABORT_ON_ERROR parameter can be used to control whether a transaction should abort or continue when an error occurs, but it does not affect detached transactions.

Snowflake uses the result cache to store the results of queries that have been executed recently. The result cache is maintained at the account level and is shared across all sessions and users. The result cache is invalidated when any changes are made to the tables or views referenced by the query. Snowflake also has a retention policy for the result cache, which determines how long the results are kept in the cache before they are purged. The default retention period for the result cache is 24 hours, but it can be changed at the account, user, or session level. However, there is a maximum retention period of 14 days for the result cache, which cannot be exceeded. Therefore, if the underlying data has not changed, Snowflake will use the persisted query results from the result cache for up to 14 days.

 This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.

The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.

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.