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NEW QUESTION 1
A database contains a table and a stored procedure defined as.

No other operations are affecting the log_table. What will be the outcome of the procedure call?

• A. The Iog_table contains zero records and the stored procedure returned 1 as a return value
• B. The Iog_table contains one record and the stored procedure returned 1 as a return value
• C. The log_table contains one record and the stored procedure returned NULL as a return value
• D. The Iog_table contains zero records and the stored procedure returned NULL as a return value

Answer: B

Explanation:
The stored procedure is defined with a FLOAT return type and a JavaScript language. The body of the stored procedure contains a SQL statement that inserts a row into the log_table with a value of ‘1’ for col1. The body also contains a return statement that returns 1 as a float value. When the stored procedure is called with any VARCHAR parameter, it will execute successfully and insert one record into the log_table and return 1 as a return value. The other options are not correct because:
✑ The log_table will not be empty after the stored procedure call, as it will contain
one record inserted by the SQL statement.
✑ The stored procedure will not return NULL as a return value, as it has an explicit return statement that returns 1.

NEW QUESTION 2
A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.
..... recommended way to ingest this data?

• A. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
• B. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
• C. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
• D. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data

Answer: D

Explanation:
The recommended way to ingest invoice data in PDF format into Snowflake
is to create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data. This option allows for more flexibility and control over how the PDF data is extracted and transformed. The other options are not suitable for ingesting PDF data into Snowflake. Option A and B are incorrect because Snowpipe and COPY INTO commands can only ingest files that are in supported file formats, such as CSV, JSON, XML, etc. PDF files are not supported by Snowflake and will cause errors or unexpected results. Option C is incorrect because external tables can only query files that are in supported file formats as well. PDF files cannot be parsed by external tables and will cause errors or unexpected results.

NEW QUESTION 3
Which Snowflake objects does the Snowflake Kafka connector use? (Select THREE).

• A. Pipe
• B. Serverless task
• C. Internal user stage
• D. Internal table stage
• E. Internal named stage
• F. Storage integration

Answer: ADE

Explanation:
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.

NEW QUESTION 4
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?

• A. Enable clustering on the table
• B. Increase the warehouse size
• C. Rewrite the query to remove the CTEs.
• D. Switch to a multi-cluster virtual warehouse

Answer: B

Explanation:
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.

NEW QUESTION 5
At what isolation level are Snowflake streams?

• A. Snapshot
• B. Repeatable read
• C. Read committed
• D. Read uncommitted

Answer: B

Explanation:
The isolation level of Snowflake streams is repeatable read, which means that each transaction sees a consistent snapshot of data that does not change during its execution. Streams use time travel internally to provide this isolation level and ensure that queries on streams return consistent results regardless of concurrent transactions on their source tables.

NEW QUESTION 6
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?

• A. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
• B. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
• C. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
• D. Use a create ok replace table as statement that references the staging table and includes all the transformation SQ
• E. Use a task to run the full create or replace table as statement on a scheduled basis

Answer: A

Explanation:
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.

NEW QUESTION 7
A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards
What will happen to the TRANSACTIONS _STM object?

• A. TRANSACTIONS _STMwill keep working as expected
• B. TRANSACTIONS _STMwill be stale and will need to be re-created
• C. TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.
• D. Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.

Answer: B

Explanation:
A stream is a Snowflake object that records the history of changes made to a table. A stream is associated with a specific table at the time of creation, and it cannot be altered to point to a different table later. Therefore, if the source table is renamed, the stream will become stale and will need to be re-created with the new table name. The other options are not correct because:
✑ TRANSACTIONS _STM will not keep working as expected, as it will lose track of
the changes made to the renamed table.
✑ TRANSACTIONS _STM will not be automatically renamed TRANSACTIONS
_RAW_STM, as streams do not inherit the name changes of their source tables.
✑ Reading from the transactions_stm stream will not succeed for some time after the expected STALE_TIME, as streams do not have a STALE_TIME property.

NEW QUESTION 8
A secure function returns data coming through an inbound share
What will happen if a Data Engineer tries to assign usage privileges on this function to an outbound share?

• A. An error will be returned because the Engineer cannot share data that has already been shared
• B. An error will be returned because only views and secure stored procedures can be shared
• C. An error will be returned because only secure functions can be shared with inboundshares
• D. The Engineer will be able to share the secure function with other accounts

Answer: A

Explanation:
An error will be returned because the Engineer cannot share data that has already been shared. A secure function is a Snowflake function that can access data from an inbound share, which is a share that is created by another account and consumed by the current account. A secure function can only be shared with an inbound share, not an outbound share, which is a share that is created by the current account and shared with other accounts. This is to prevent data leakage or unauthorized access to the data from the inbound share.

NEW QUESTION 9
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?
A)


C)

D)

• A. Option A
• B. Option B
• C. Option C
• D. Option D

Answer: B

Explanation:
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = ‘North America’. Therefore, the function call in Option B will return the desired result.

NEW QUESTION 10
A Data Engineer wants to create a new development database (DEV) as a clone of the permanent production database (PROD) There is a requirement to disable Fail-safe for all tables.
Which command will meet these requirements?

• A. CREATE DATABASE DEV CLONE PROD FAIL_SAFE=FALSE;
• B. CREATE DATABASE DEV CLONE PROD;
• C. CREATE TRANSIENT DATABASE DEV CLONE RPOD
• D. CREATE DATABASE DEV CLOSE PRODDATA_RETENTION_TIME_IN_DAYS =0L

Answer: C

Explanation:
This option will meet the requirements of creating a new development database (DEV) as a clone of the permanent production database (PROD) and disabling Fail-safe for all tables. By using the CREATE TRANSIENT DATABASE command, the Data Engineer can create a transient database that does not have Fail-safe enabled by default. Fail-safe is a feature in Snowflake that provides additional protection against data loss by retaining historical data for seven days beyond the time travel retention period. Transient databases do not have Fail-safe enabled, which means that they do not incur additional storage costs for historical data beyond their time travel retention period. By using the CLONE option, the Data Engineer can create an exact copy of the PROD database, including its schemas, tables, views, and other objects.

NEW QUESTION 11
Database XYZ has the data_retention_time_in_days parameter set to 7 days and table xyz.public.ABC has the data_retention_time_in_daysset to 10 days.
A Developer accidentally dropped the database containing this single table 8 days ago and just discovered the mistake.
How can the table be recovered?

• A. undrop database xyz;
• B. create -able abc_restore as select * from xyz.public.abc at {offset => -60*60*24*8};
• C. create table abc_restore clone xyz.public.abc at (offset => -3€0G*24*3);
• D. Create a Snowflake Support case lo restore the database and tab e from "a i-safe

Answer: A

Explanation:
The table can be recovered by using the undrop database xyz; command. This command will restore the database that was dropped within the last 14 days, along with all its schemas and tables, including the customer table. The data_retention_time_in_days parameter does not affect this command, as it only applies to time travel queries that reference historical data versions of tables or databases. The other options are not valid ways to recover the table. Option B is incorrect because creating a table as select * from xyz.public.ABC at {offset => -6060248} will not work, as this query will try to access a historical data version of the ABC table that does not exist anymore after dropping the database. Option C is incorrect because creating a table clone xyz.public.ABC at {offset => -360024*3} will not work, as this query will try to clone a historical data version of the ABC table that does not exist anymore after dropping the database. Option D is incorrect because creating a Snowflake Support case to restore the database and table from fail-safe will not work, as fail-safe is only available for disaster recovery scenarios and cannot be accessed by customers.

NEW QUESTION 12
A Data Engineer has developed a dashboard that will issue the same SQL select clause to Snowflake every 12 hours.
---will Snowflake use the persisted query results from the result cache provided that the underlying data has not changed^

• A. 12 hours
• B. 24 hours
• C. 14 days
• D. 31 days

Answer: C

Explanation:
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.

NEW QUESTION 13
Which use case would be BEST suited for the search optimization service?

• A. Analysts who need to perform aggregates over high cardinality columns
• B. Business users who need fast response times using highly selective filters
• C. Data Scientists who seek specific JOIN statements with large volumes of data
• D. Data Engineers who create clustered tables with frequent reads against clustering keys

Answer: B

Explanation:
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.

NEW QUESTION 14
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)

• A. session.jdbc_connection()
• B. session.read.json{)
• C. session,table()
• D. DataFraas.writeO
• E. session.builder()
• F. session.sql()

Answer: BCF

Explanation:
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.

NEW QUESTION 15
Which stages support external tables?

• A. Internal stages only; within a single Snowflake account
• B. internal stages only from any Snowflake account in the organization
• C. External stages only from any region, and any cloud provider
• D. External stages only, only on the same region and cloud provider as the Snowflake account

Answer: C

Explanation:
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.

NEW QUESTION 16
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