Certification Topics of ARA-C01 Exam PDF Recently Updated Questions [Q91-Q107]

Certification Topics of ARA-C01 Exam PDF Recently Updated Questions

ARA-C01 Exam Prep Guide: Prep guide for the ARA-C01 Exam

Snowflake ARA-C01 exam is one of the most sought-after certifications in the data analytics industry. It provides professionals with the opportunity to demonstrate their expertise in Snowflake's advanced concepts and techniques. SnowPro Advanced Architect Certification certification program is designed to validate the candidate's knowledge of data warehousing, data modeling, ETL, security, and performance optimization best practices in a Snowflake environment.

 

NEW QUESTION # 91
A company has a Snowflake environment running in AWS us-west-2 (Oregon). The company needs to share data privately with a customer who is running their Snowflake environment in Azure East US 2 (Virginia).
What is the recommended sequence of operations that must be followed to meet this requirement?

• A. 1. Create a share and add the database privileges to the share
  2. Create a new listing on the Snowflake Marketplace
  3. Alter the listing and add the share
  4. Instruct the customer to subscribe to the listing on the Snowflake Marketplace
• B. 1. Create a new Snowflake account in Azure East US 2 (Virginia)
  2. Set up replication between AWS us-west-2 (Oregon) and Azure East US 2 (Virginia) for the database objects to be shared
  3. Create a share and add the database privileges to the share
  4. Alter the share and add the customer's Snowflake account to the share
• C. 1. Ask the customer to create a new Snowflake account in Azure EAST US 2 (Virginia)
  2. Create a share and add the database privileges to the share
  3. Alter the share and add the customer's Snowflake account to the share
• D. 1. Create a reader account in Azure East US 2 (Virginia)
  2. Create a share and add the database privileges to the share
  3. Add the reader account to the share
  4. Share the reader account's URL and credentials with the customer

Answer: B

Explanation:
Option C is the correct answer because it allows the company to share data privately with the customer across different cloud platforms and regions. The company can create a new Snowflake account in Azure East US 2 (Virginia) and set up replication between AWS us-west-2 (Oregon) and Azure East US 2 (Virginia) for the database objects to be shared. This way, the company can ensure that the data is always up to date and consistent in both accounts. The company can then create a share and add the database privileges to the share, and alter the share and add the customer's Snowflake account to the share. The customer can then access the shared data from their own Snowflake account in Azure East US 2 (Virginia).
Option A is incorrect because the Snowflake Marketplace is not a private way of sharing data. The Snowflake Marketplace is a public data exchange platform that allows anyone to browse and subscribe to data sets from various providers. The company would not be able to control who can access their data if they use the Snowflake Marketplace.
Option B is incorrect because it requires the customer to create a new Snowflake account in Azure East US 2 (Virginia), which may not be feasible or desirable for the customer. The customer may already have an existing Snowflake account in a different cloud platform or region, and may not want to incur additional costs or complexity by creating a new account.
Option D is incorrect because it involves creating a reader account in Azure East US 2 (Virginia), which is a limited and temporary way of sharing data. A reader account is a special type of Snowflake account that can only access data from a single share, and has a fixed duration of 30 days. The company would have to manage the reader account's URL and credentials, and renew the account every 30 days. The customer would not be able to use their own Snowflake account to access the shared data, and would have to rely on the company's reader account.
Reference:
Snowflake Replication
Secure Data Sharing Overview
Snowflake Marketplace Overview
Reader Account Overview

NEW QUESTION # 92
A company's client application supports multiple authentication methods, and is using Okta.
What is the best practice recommendation for the order of priority when applications authenticate to Snowflake?

• A. 1) Okta native authentication
  2) Key Pair Authentication, mostly used for production environment users
  3) Password
  4) OAuth (either Snowflake OAuth or External OAuth)
  5) External browser, SSO
• B. 1) OAuth (either Snowflake OAuth or External OAuth)
  2) External browser
  3) Okta native authentication
  4) Key Pair Authentication, mostly used for service account users
  5) Password
• C. 1) Password
  2) Key Pair Authentication, mostly used for production environment users
  3) Okta native authentication
  4) OAuth (either Snowflake OAuth or External OAuth)
  5) External browser, SSO
• D. 1) External browser, SSO
  2) Key Pair Authentication, mostly used for development environment users
  3) Okta native authentication
  4) OAuth (ether Snowflake OAuth or External OAuth)
  5) Password

Answer: B

Explanation:
Explanation
* This is the best practice recommendation for the order of priority when applications authenticate to Snowflake, according to the Snowflake documentation and the web search results. Authentication is the process of verifying the identity of a user or application that connects to Snowflake. Snowflake supports multiple authentication methods, each with different advantages and disadvantages. The recommended order of priority is based on the following factors:
* Security: The authentication method should provide a high level of security and protection against unauthorized access or data breaches. The authentication method should also support multi-factor authentication (MFA) or single sign-on (SSO) for additional security.
* Convenience: The authentication method should provide a smooth and easy user experience, without requiring complex or manual steps. The authentication method should also support seamless integration with external identity providers or applications.
* Flexibility: The authentication method should provide a range of options and features to suit different use cases and scenarios. The authentication method should also support customization and configuration to meet specific requirements.
Based on these factors, the recommended order of priority is:
* OAuth (either Snowflake OAuth or External OAuth): OAuth is an open standard for authorization that allows applications to access Snowflake resources on behalf of a user, without exposing the user's credentials. OAuth provides a high level of security, convenience, and flexibility, as it supports MFA, SSO, token-based authentication, and various grant types and scopes. OAuth can be implemented using either Snowflake OAuth or External OAuth, depending on the identity provider and the application12.
* External browser: External browser is an authentication method that allows users to log in to Snowflake using a web browser and an external identity provider, such as Okta, Azure AD, or Ping Identity.
External browser provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. External browser also provides a consistent user interface and experience across different platforms and devices34.
* Okta native authentication: Okta native authentication is an authentication method that allows users to log in to Snowflake using Okta as the identity provider, without using a web browser. Okta native authentication provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. Okta native authentication also provides a native user interface and experience for Okta users, and supports various Okta features, such as password policies and user management56.
* Key Pair Authentication: Key Pair Authentication is an authentication method that allows users to log in to Snowflake using a public-private key pair, without using a password. Key Pair Authentication provides a high level of security, as it relies on asymmetric encryption and digital signatures. Key Pair Authentication also provides a flexible and customizable authentication option, as it supports various key formats, algorithms, and expiration times. Key Pair Authentication is mostly used for service account users, such as applications or scripts that connect to Snowflake programmatically7 .
* Password: Password is the simplest and most basic authentication method that allows users to log in to Snowflake using a username and password. Password provides a low level of security, as it relies on symmetric encryption and is vulnerable to brute force attacks or phishing. Password also provides a low level of convenience and flexibility, as it requires manual input and management, and does not support MFA or SSO. Password is the least recommended authentication method, and should be used only as a last resort or for testing purposes .
References:
* Snowflake Documentation: Snowflake OAuth
* Snowflake Documentation: External OAuth
* Snowflake Documentation: External Browser Authentication
* Snowflake Blog: How to Use External Browser Authentication with Snowflake
* Snowflake Documentation: Okta Native Authentication
* Snowflake Blog: How to Use Okta Native Authentication with Snowflake
* Snowflake Documentation: Key Pair Authentication
* [Snowflake Blog: How to Use Key Pair Authentication with Snowflake]
* [Snowflake Documentation: Password Authentication]
* [Snowflake Blog: How to Use Password Authentication with Snowflake]

NEW QUESTION # 93
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

• A. Table
• B. Global
• C. Schema
• D. Database

Answer: B

Explanation:
The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges can be granted is global. These are account-level privileges that control who can apply or unset these policies on objects such as columns, tables, views, accounts, or users. These privileges are granted to the ACCOUNTADMIN role by default, and can be granted to other roles as needed. The other options are incorrect because they are not the object type level at which these privileges can be granted. Database, schema, and table are lower-level object types that do not support these privileges. Reference: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

NEW QUESTION # 94
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

• A. Storage costs must be optimized.
• B. There needs to be fewer objects per tenant.
• C. Compute costs must be optimized.
• D. Security and Role-Based Access Control (RBAC) policies must be simple to configure.
• E. Tenant data shape may be unique per tenant.

Answer: D,E

Explanation:
The Account Per Tenant strategy involves creating separate Snowflake accounts for each tenant within the multi-tenant application. This approach offers a number of advantages.
Option B: With separate accounts, each tenant's environment is isolated, making security and RBAC policies simpler to configure and maintain. This is because each account can have its own set of roles and privileges without the risk of cross-tenant access or the complexity of maintaining a highly granular permission model within a shared environment.
Option D: This approach also allows for each tenant to have a unique data shape, meaning that the database schema can be tailored to the specific needs of each tenant without affecting others. This can be essential when tenants have different data models, usage patterns, or application customizations.

NEW QUESTION # 95
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. 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.
• B. 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.
• 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: A

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. References:
* 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 # 96
What are some of the characteristics of result set caches? (Choose three.)

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

Answer: A,C,F

Explanation:
Comprehensive and Detailed Explanation: According to the SnowPro Advanced: Architect documents and learning resources, some of the characteristics of result set caches are:
* Snowflake persists the data results for 24 hours. This means that the result set cache holds the results of every query executed in the past 24 hours, and can be reused if the same query is submitted again and the underlying data has not changed1.
* Each time persisted results for a query are used, a 24-hour retention period is reset. This means that the result set cache extends the lifetime of the results every time they are reused, up to a maximum of 31 days from the date and time that the query was first executed1.
* The retention period can be reset for a maximum of 31 days. This means that the result set cache will purge the results after 31 days, regardless of whether they are reused or not. After 31 days, the next time the query is submitted, a new result is generated and persisted1.
The other options are incorrect because they are not characteristics of result set caches. Option A is incorrect because Time Travel queries cannot be executed against the result set cache. Time Travel queries use the AS OF clause to access historical data that is stored in the storage layer, not the result set cache2. Option D is incorrect because the data stored in the result set cache does not contribute to storage costs. The result set cache is maintained by the service layer, and does not incur any additional charges1. Option F is incorrect because the result set cache is shared between warehouses. The result set cache is available across virtual warehouses, so query results returned to one user are available to any other user on the system who executes the same query, provided the underlying data has not changed1. References: Using Persisted Query Results | Snowflake Documentation, Time Travel | Snowflake Documentation

NEW QUESTION # 97
A company is using a Snowflake account in Azure. The account has SAML SSO set up using ADFS as a SCIM identity provider. To validate Private Link connectivity, an Architect performed the following steps:
* Confirmed Private Link URLs are working by logging in with a username/password account
* Verified DNS resolution by running nslookups against Private Link URLs
* Validated connectivity using SnowCD
* Disabled public access using a network policy set to use the company's IP address range However, the following error message is received when using SSO to log into the company account:
IP XX.XXX.XX.XX is not allowed to access snowflake. Contact your local security administrator.
What steps should the Architect take to resolve this error and ensure that the account is accessed using only Private Link? (Choose two.)

• A. Alter the Azure security integration to use the Private Link URLs.
• B. Open a case with Snowflake Support to authorize the Private Link URLs' access to the account.
• C. Add the IP address in the error message to the allowed list in the network policy.
• D. Generate a new SCIM access token using system$generate_scim_access_token and save it to Azure AD.
• E. Update the configuration of the Azure AD SSO to use the Private Link URLs.

Answer: C,E

Explanation:
Explanation
The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:
* Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
* Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.
These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs' access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.

NEW QUESTION # 98
A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.
The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.
What step can be taken to improve the pruning of the reporting tables?

• A. Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
• B. Use an ORDER BY <cluster_key (s) > command to load the reporting tables.
• C. Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.
• D. Increase the size of the virtual warehouse to a size 5X-Large.

Answer: B

Explanation:
Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.
References =
*Snowflake Documentation on micro-partitions and data clustering2
*Community article on recognizing unsatisfactory pruning and improving it1

NEW QUESTION # 99
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account? (Select THREE).

• A. Table
• B. Role
• C. Database
• D. Stage
• E. Schema
• F. Warehouse

Answer: B,C,F

NEW QUESTION # 100
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?

• A. The query Is queued for execution.
• B. The query Is reading from remote storage
• C. The query is processing a very large dataset.
• D. The query has overly complex logic.

Answer: D

Explanation:
* The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
* Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
* Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts
* running. The query queue time depends on the warehouse load, concurrency, and priority settings.
* Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
* Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
* Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
* What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.

NEW QUESTION # 101
When does a multi-cluster warehouse shuts down with default scaling policy

• A. After 2 to 3 consecutive successful checks (performed at 1 minute intervals), which determine whether the load on the least-loaded cluster could be redistributed to the other clusters without spinning up the cluster again.
• B. After 5 to 6 consecutive successful checks (performed at 1 minute intervals), which determine whether the load on the least-loaded cluster could be redistributed to the other clusters without spinning up the cluster again.
• C. Immediately after the query is run

Answer: A

NEW QUESTION # 102
A Snowflake Architect Is working with Data Modelers and Table Designers to draft an ELT framework specifically for data loading using Snowpipe. The Table Designers will add a timestamp column that Inserts the current tlmestamp as the default value as records are loaded into a table. The Intent is to capture the time when each record gets loaded into the table; however, when tested the timestamps are earlier than the loae_take column values returned by the copy_history function or the Copy_HISTORY view (Account Usage).
Why Is this occurring?

• A. The Snowflake timezone parameter Is different from the cloud provider's parameters causing the mismatch.
• B. The timestamps are different because there are parameter setup mismatches. The parameters need to be realigned
• C. The CURRENT_TIMEis evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table.
• D. The Table Designer team has not used the localtimestamp or systimestamp functions in the Snowflake copy statement.

Answer: C

Explanation:
The correct answer is D because the CURRENT_TIME function returns the current timestamp at the start of the statement execution, not at the time of the record insertion. Therefore, if the load operation takes some time to complete, the CURRENT_TIME value may be earlier than the actual load time.
Option A is incorrect because the parameter setup mismatches do not affect the timestamp values. The parameters are used to control the behavior and performance of the load operation, such as the file format, the error handling, the purge option, etc.
Option B is incorrect because the Snowflake timezone parameter and the cloud provider's parameters are independent of each other. The Snowflake timezone parameter determines the session timezone for displaying and converting timestamp values, while the cloud provider's parameters determine the physical location and configuration of the storage and compute resources.
Option C is incorrect because the localtimestamp and systimestamp functions are not relevant for the Snowpipe load operation. The localtimestamp function returns the current timestamp in the session timezone, while the systimestamp function returns the current timestamp in the system timezone. Neither of them reflect the actual load time of the records. 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.
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.

NEW QUESTION # 103
An Architect is troubleshooting a query with poor performance using the QUERY function. The Architect observes that the COMPILATION_TIME Is greater than the EXECUTION_TIME.
What is the reason for this?

• A. The query Is queued for execution.
• B. The query Is reading from remote storage
• C. The query is processing a very large dataset.
• D. The query has overly complex logic.

Answer: D

Explanation:
* The correct answer is B because the compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. The compilation time depends on the complexity of the query, such as the number of tables, columns, joins, filters, aggregations, subqueries, etc. The more complex the query, the longer it takes to compile.
* Option A is incorrect because the query processing time is not affected by the size of the dataset, but by the size of the virtual warehouse. Snowflake automatically scales the compute resources to match the data volume and parallelizes the query execution. The size of the dataset may affect the execution time, but not the compilation time.
* Option C is incorrect because the query queue time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query waits for a warehouse slot before it starts running. The query queue time depends on the warehouse load, concurrency, and priority settings.
* Option D is incorrect because the query remote IO time is not part of the compilation time or the execution time. It is a separate metric that indicates how long the query spends reading data from remote storage, such as S3 or Azure Blob Storage. The query remote IO time depends on the network latency, bandwidth, and caching efficiency. References:
* Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time: This article explains why the total duration (compilation + execution) time is an essential metric to measure query performance in Snowflake. It discusses the reasons for the long compilation time, including query complexity and the number of tables and columns.
* Exploring Execution Times: This document explains how to examine the past performance of queries and tasks using Snowsight or by writing queries against views in the ACCOUNT_USAGE schema. It also describes the different metrics and dimensions that affect query performance, such as duration, compilation, execution, queue, and remote IO time.
* What is the "compilation time" and how to optimize it?: This community post provides some tips and best practices on how to reduce the compilation time, such as simplifying the query logic, using views or common table expressions, and avoiding unnecessary columns or joins.

NEW QUESTION # 104
One of your query is taking a long time to finish, when you open the query profiler you see that lot of data is spilling to the remote disk(Bytes spilled to remote storage).
What may be the cause of this?

• A. The amount of memory available for the servers used to execute the operation might not be sufficient to hold intermediate results
• B. Number of disks attached to the virtual warehouse is not enough for the processing
• C. The size of the AWS bucket used to hold the data is not sufficient for the query

Answer: A

NEW QUESTION # 105
A company's Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes.
What would be the MOST efficient solution?

• A. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.
• B. Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).
• C. Ask the partner to create a share and add the company's account.
• D. Keep the current structure but request that the partner stop changing files, instead only appending new files.

Answer: C

Explanation:
The most efficient solution is to ask the partner to create a share and add the company's account (Option A).
This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake's secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases. Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO <location> command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently. Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues. Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive. References: The answer can be verified from Snowflake's official documentation on secure data sharing, data lake export, and reader accounts available on their website. Here are some relevant links:
* Introduction to Secure Data Sharing | Snowflake Documentation
* Data Lake Export Public Preview Is Now Available on Snowflake | Snowflake Blog
* Managing Reader Accounts | Snowflake Documentation

NEW QUESTION # 106
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?

• A.
• B.
• C.
• D.

Answer: D

NEW QUESTION # 107
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One of the unique features of the SnowPro Advanced Architect Certification exam is that it is an online, proctored exam. This means that candidates can take the exam from anywhere in the world, at any time, while being monitored by a proctor through a webcam. This format ensures the integrity and security of the exam, while also providing convenience and flexibility to candidates.

 

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