ARA-C01 Dumps PDF New [2024] Ultimate Study Guide [Q21-Q37]

ARA-C01 Dumps PDF New [2024] Ultimate Study Guide

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly reputable certification that is recognized globally by businesses and organizations that use Snowflake. SnowPro Advanced Architect Certification certification exam is designed to test the skills and knowledge of individuals who want to become advanced architects in data warehousing and data analytics. SnowPro Advanced Architect Certification certification is a valuable asset for individuals who want to advance their careers in these fields, and there are several resources available to help candidates prepare for the exam.

 

NEW QUESTION # 21
Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

This command results in the following error:
SQL compilation error: invalid parameter 'validation_mode'
Assuming the syntax is correct, what is the cause of this error?

• A. The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.
• B. The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.
• C. The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.
• D. The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.

Answer: D

Explanation:
The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.
The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.
According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1. Therefore, option C is the correct answer.

NEW QUESTION # 22
Bytes spilled to remote storage in query profile indicates volume of data spilled to remote disk

• A. FALSE
• B. TRUE

Answer: B

NEW QUESTION # 23
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?

• A. ABORT_STATEMENT
• B. SKIP_FILE
• C. FAIL
• D. CONTINUE

Answer: A,B,D

Explanation:
The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
Therefore, options A, B, and C are correct.

NEW QUESTION # 24
Which of the below commands will use warehouse credits?

• A. SHOW TABLES LIKE 'SNOWFL%';
• B. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
• C. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
• D. SELECT COUNT(*) FROM SNOWFLAKE;

Answer: B,C,D

Explanation:
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.

NEW QUESTION # 25
What are purposes for creating a storage integration? (Choose three.)

• A. Manage credentials from multiple cloud providers in one single Snowflake object.
• B. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.
• C. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.
• D. Support multiple external stages using one single Snowflake object.
• E. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
• F. Avoid supplying credentials when creating a stage or when loading or unloading data.

Answer: B,D,F

NEW QUESTION # 26
You can define a clustering key directly on top of VARIANT columns

• A. FALSE
• B. TRUE

Answer: A

NEW QUESTION # 27
For this object, Snowflake executes code outside Snowflake; the executed code is known as remote service.
What is this object called?

• A. External procedure
• B. External Script
• C. External function
• D. External job

Answer: C

NEW QUESTION # 28
Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).

• A. Developers create their own datasets to work against transformed versions of the live data.
• B. Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region.
• C. The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account.
• D. Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.
• E. Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing.

Answer: A,B

Explanation:
Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently. Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2 However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions. In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3 The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:
Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values. Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4 Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account. To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5 The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:
Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database. To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6 Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database. The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7 The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database. The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8 Reference:
1: SnowPro Advanced: Architect | Study Guide 9
2: Snowflake Documentation | Cloning Overview
3: Snowflake Documentation | Loading Data Using COPY into a Table
4: Snowflake Documentation | Transforming Data During a Load
5: Snowflake Documentation | Data Sharing Overview
6: Snowflake Documentation | Secure Views
7: Snowflake Documentation | Cloning Databases, Schemas, and Tables
8: Snowflake Documentation | Cloning for Testing and Development
: SnowPro Advanced: Architect | Study Guide
: Cloning Overview
: Loading Data Using COPY into a Table
: Transforming Data During a Load
: Data Sharing Overview
: Secure Views
: Cloning Databases, Schemas, and Tables
: Cloning for Testing and Development

NEW QUESTION # 29
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

• A. A STREAM object
• B. The UPSERT command
• C. The MERGE command
• D. The CHANGE_DATA_CAPTURE command
• E. The CHANGES clause

Answer: A,E

Explanation:
The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. Reference: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation

NEW QUESTION # 30
A user who has SELECT privilege on a view does not also need SELECT privilege on the tables that the view uses

• A. FALSE
• B. TRUE

Answer: B

NEW QUESTION # 31
What are some of the characteristics of result set caches? (Choose three.)

• A. Each time persisted results for a query are used, a 24-hour retention period is reset.
• B. Snowflake persists the data results for 24 hours.
• C. The data stored in the result cache will contribute to storage costs.
• D. Time Travel queries can be executed against the result set cache.
• E. The result set cache is not shared between warehouses.
• F. The retention period can be reset for a maximum of 31 days.

Answer: A,B,F

NEW QUESTION # 32
What are purposes for creating a storage integration? (Choose three.)

• A. Manage credentials from multiple cloud providers in one single Snowflake object.
• B. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.
• C. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.
• D. Support multiple external stages using one single Snowflake object.
• E. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
• F. Avoid supplying credentials when creating a stage or when loading or unloading data.

Answer: B,D,F

Explanation:
A storage integration is a Snowflake object that stores a generated identity and access management (IAM) entity for an external cloud provider, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. This integration allows Snowflake to read data from and write data to an external storage location referenced in an external stage1.
One purpose of creating a storage integration is to support multiple external stages using one single Snowflake object. An integration can list buckets (and optional paths) that limit the locations users can specify when creating external stages that use the integration. Note that many external stage objects can reference different buckets and paths and use the same storage integration for authentication1. Therefore, option C is correct.
Another purpose of creating a storage integration is to avoid supplying credentials when creating a stage or when loading or unloading data. Integrations are named, first-class Snowflake objects that avoid the need for passing explicit cloud provider credentials such as secret keys or access tokens. Integration objects store an IAM user ID, and an administrator in your organization grants the IAM user permissions in the cloud provider account1. Therefore, option D is correct.
A third purpose of creating a storage integration is to store a generated IAM entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account. For example, you can create a storage integration for Amazon S3 even if your Snowflake account is hosted on Azure or Google Cloud Platform. This allows you to access data across different cloud platforms using Snowflake1. Therefore, option B is correct.
Option A is incorrect, because creating a storage integration does not control access to Snowflake data using a master encryption key. Snowflake encrypts all data using a hierarchical key model, and the master encryption key is managed by Snowflake or by the customer using a cloud provider's key management service. This is independent of the storage integration feature2.
Option E is incorrect, because creating a storage integration does not create private VPC endpoints. Private VPC endpoints are a network configuration option that allow direct, secure connectivity between VPCs without traversing the public internet. This is also independent of the storage integration feature3.
Option F is incorrect, because creating a storage integration does not manage credentials from multiple cloud providers in one single Snowflake object. A storage integration is specific to one cloud provider, and you need to create separate integrations for each cloud provider you want to access4.

NEW QUESTION # 33
An Architect Is designing a data lake with Snowflake. The company has structured, semi-structured, and unstructured dat a. The company wants to save the data inside the data lake within the Snowflake system. The company is planning on sharing data among Its corporate branches using Snowflake data sharing.
What should be considered when sharing the unstructured data within Snowflake?

• A. A pre-signed URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with no time limit for the URL.
• B. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with the "expiration_time" argument defined for the URL time limit.
• C. A scoped URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 24-hour time limit for the URL.
• D. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 7-day time limit for the URL.

Answer: B

Explanation:
According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. Reference:
Sharing unstructured Data with a secure view
Introduction to Loading Unstructured Data

NEW QUESTION # 34
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

• A. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.
• B. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.
• C. All rows loaded using a specific COPY statement will have the same timestamp value.
• D. All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.

Answer: C

Explanation:
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source. Reference:
Snowflake Documentation: Loading Data into Tables with Default Values
Snowflake Documentation: COPY INTO table

NEW QUESTION # 35
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.
• B. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-05-01 10:00:00');
• C. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• D. Undrop the STAGING schema and then rerun the CLONE statement.

Answer: C

NEW QUESTION # 36
A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field.
What can be done to improve performance?

• A. Validate the size of the warehouse being used. If the record count is approaching 100s of millions, size XL will be the minimum size required to process this amount of data.
• B. Alter the target table to Include additional fields pulled from the JSON records. This would Include a create_date field with a datatype of time stamp. When this field Is used in the filter, partition pruning will occur.
• C. Incorporate the use of multiple tables partitioned by date ranges. When a user or process needs to query a particular date range, ensure the appropriate base table Is used.
• D. Alter the target table to include additional fields pulled from the JSON records. This would include a create_date field with a datatype of varchar. When this field is used in the filter, partition pruning will occur.

Answer: B

Explanation:
The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.
Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.
Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.
Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. Reference:
Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.
Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.
Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.
Snowflake Documentation: Optimizing Storage for Performance: This document explains how to optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.

NEW QUESTION # 37
......

To become SnowPro Advanced Architect certified, candidates must pass the Snowflake ARA-C01 exam. ARA-C01 exam is designed to test the candidate's ability to design and implement advanced Snowflake solutions, as well as their ability to troubleshoot and optimize Snowflake implementations. The SnowPro Advanced Architect certification is a valuable credential for architects and engineers who work with the Snowflake platform, as it demonstrates their expertise in designing and implementing complex Snowflake solutions. SnowPro Advanced Architect Certification certification is recognized by Snowflake and its partners and is a valuable asset for those looking to advance their career in the data warehousing and data analytics space.

Snowflake ARA-C01: SnowPro Advanced Architect Certification is a highly recognized and sought-after certification for individuals who want to demonstrate their advanced knowledge and expertise in Snowflake's cloud data platform. SnowPro Advanced Architect Certification certification is designed for architects and advanced-level professionals who have experience in designing and implementing complex data solutions with Snowflake.

 

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