Most Recent Snowflake ARA-C01 Exam Dumps

According to the Snowflake documentation, the best practices for choosing clustering keys are:

Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.

Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.

Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.

Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.

Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.

Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.

Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.

Clustering Keys & Clustered Tables | Snowflake Documentation

[Considerations for Choosing Clustering for a Table | Snowflake Documentation]

These three design elements are required to meet the security, compliance, and governance requirements for the project.

To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions.Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer-managed key to create a composite master key to encrypt the data in Snowflake1.

To share some information stored in a view with another Snowflake customer, a secure view is recommended. A secure view is a view that hides the underlying data and the view definition from unauthorized users.Only the owner of the view and the users who are granted the owner's role can see the view definition and the data in the base tables of the view2.A secure view can be shared with another Snowflake account using a data share3.

To hide portions of sensitive information from some columns, Dynamic Data Masking can be used. Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time.Depending on the masking policy conditions and the user's role, the data can be fully or partially masked, or shown as plain-text4.

The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.

To copy data from DB1 to TBL2, there are two possible options among the choices given:

Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1.The external stage can be created using theCREATE STAGEcommand with theURLparameter specifying the location of STAGE11. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

create stage EXT_STAGE1

url = @DB1.SH1.STAGE1;

Then, the data can be copied from the external stage to TBL2 using theCOPY INTOcommand with theFROMparameter specifying the external stage name and theFILE FORMATparameter specifying the file format name2. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

copy into TBL2

from @EXT_STAGE1

file format = (format name = DB1.SH1.FF PIPE 1);

Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using theINSERT INTOcommand with theSELECTclause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2.The query must use the fully-qualified names of the tables, including the database and schema names3. For example:

SQLAI-generated code. Review and use carefully.More info on FAQ.

use database DB2;

use schema SH2;

insert into TBL2

select * from DB1.SH1.TBL1;

The other options are not valid because:

Option A: It uses an invalid syntax for theCOPY INTOcommand.TheFROMparameter cannot specify a table name, only a stage name or a file location2.

Option C: It uses an invalid syntax for theCOPY INTOcommand.TheFILE FORMATparameter cannot specify a stage name, only a file format name or options2.

Option D: It uses an invalid syntax for theCREATE STAGEcommand.TheURLparameter cannot specify a table name, only a file location1.

1: CREATE STAGE | Snowflake Documentation

2: COPY INTO table | Snowflake Documentation

3: Cross-database Queries | Snowflake Documentation

Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations.Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.

Among the commands listed in the question, the following ones will use warehouse credits:

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute.The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.

SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute.The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute.The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.

The command that will not use warehouse credits is:

SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute.The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.

Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage.Snowpipe also provides REST endpoints to load data and retrieve load history reports1.

The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range.The endpoint accepts the following parameters2:

pipe: The fully-qualified name of the pipe to query.

startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.

endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.

recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.

showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.

The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe.The other options are either too infrequent, too narrow, or use the wrong endpoint3.

1: Introduction to Snowpipe | Snowflake Documentation

2: loadHistoryScan | Snowflake Documentation

3: Monitoring Snowpipe Load History | Snowflake Documentation