Get Ready with ARA-C01 Exam Dumps (2026) [Q71-Q94]

Get Ready with ARA-C01 Exam Dumps (2026)

Realistic ARA-C01 Dumps are Available for Instant Access

To prepare for the SnowPro Advanced Architect Certification Exam, candidates must have a strong foundation in Snowflake architecture and design principles. They must also have practical experience in implementing Snowflake solutions in real-world scenarios. ARA-C01 exam consists of multiple-choice questions and performance-based tasks that require candidates to apply their knowledge of Snowflake architecture to solve complex problems. Successful candidates receive a SnowPro Advanced Architect Certification, which is valid for two years and can be renewed by passing a recertification exam.

 

NEW QUESTION # 71
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?

• A. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.
• B. Unmasked data will be loaded in the new column.
• C. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.
• D. Masked data will be loaded into the new column.

Answer: B

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well.
The masking policy does not affect the underlying data in the column, only the query results.
References:
* Snowflake Documentation: Column Masking
* Snowflake Learning: Column Masking

NEW QUESTION # 72
Which of the below commands will use warehouse credits?

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

Answer: A

NEW QUESTION # 73
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

• A. Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.
• B. The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.
• C. The Connector only works in Snowflake regions that use AWS infrastructure.
• D. The Connector creates and manages its own stage, file format, and pipe objects.

Answer: D

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

NEW QUESTION # 74
A company needs to share its product catalog data with one of its partners. The product catalog data is stored in two database tables: product_category, and product_details. Both tables can be joined by the product_id column. Data access should be governed, and only the partner should have access to the records.
The partner is not a Snowflake customer. The partner uses Amazon S3 for cloud storage.
Which design will be the MOST cost-effective and secure, while using the required Snowflake features?

• A. Create a reader account for the partner and share the data sets as secure views.
• B. Publish product_category and product_details data sets on the Snowflake Marketplace.
• C. Use Secure Data Sharing with an S3 bucket as a destination.
• D. Create a database user for the partner and give them access to the required data sets.

Answer: B

NEW QUESTION # 75
Which command can be run to list all shares that have been created in your account or are available to consume by your account

• A. DESCRIBE SHARES
• B. SHOW SHARES
• C. LIST SHARES

Answer: B

NEW QUESTION # 76
A company has several sites in different regions from which the company wants to ingest data.
Which of the following will enable this type of data ingestion?

• A. The company should use a storage integration for the external stage.
• B. The company must have a Snowflake account in each cloud region to be able to ingest data to that account.
• C. The company should provision a reader account to each site and ingest the data through the reader accounts.
• D. The company must replicate data between Snowflake accounts.

Answer: A

Explanation:
This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.
Reference:
Snowflake Documentation: Storage Integrations
Snowflake Documentation: External Stages

NEW QUESTION # 77
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. Compute costs must be optimized.
• B. There needs to be fewer objects per tenant.
• C. Security and Role-Based Access Control (RBAC) policies must be simple to configure.
• D. Storage costs must be optimized.
• E. Tenant data shape may be unique per tenant.

Answer: D,E

Explanation:
Explanation
* An Account Per Tenant strategy means creating a separate Snowflake account for each tenant (customer or business unit) of the multi-tenant application.
* This approach has some advantages and disadvantages compared to other strategies, such as Database Per Tenant or Schema Per Tenant.
* One advantage is that each tenant can have a unique data shape, meaning they can define their own tables, views, and other objects without affecting other tenants. This allows for more flexibility and customization for each tenant. Therefore, option D is correct.
* Another advantage is that storage costs can be optimized, because each tenant can use their own storage credits and manage their own data retention policies. This also reduces the risk of data spillover or cross-tenant access. Therefore, option E is correct.
* However, this approach also has some drawbacks, such as:
* It requires more administrative overhead and complexity to manage multiple accounts and their resources.
* It may not optimize compute costs, because each tenant has to provision their own warehouses and pay for their own compute credits. This may result in underutilization or overprovisioning of compute resources. Therefore, option C is incorrect.
* It may not simplify security and RBAC policies, because each account has to define its own roles, users, and privileges. This may increase the risk of human errors or inconsistencies in security configurations. Therefore, option B is incorrect.
* It may not reduce the number of objects per tenant, because each tenant still has to create their own databases, schemas, and other objects within their account. This mayaffect the performance and scalability of the application. Therefore, option A is incorrect.
References: : Multi-Tenant Application Strategies

NEW QUESTION # 78
What integration object should be used to place restrictions on where data may be exported?

• A. API integration
• B. Storage integration
• C. Stage integration
• D. Security integration

Answer: D

Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the integration object that should be used to place restrictions on where data may be exported is the security integration. A security integration is a Snowflake object that provides an interface between Snowflake and third-party security services, such as Okta, Duo, or Google Authenticator. A security integration can be used to enforce policies on data export, such as requiring multi-factor authentication (MFA) or restricting the export destination to a specific network or domain. A security integration can also be used to enable single sign-on (SSO) or federated authentication for Snowflake users1.
The other options are incorrect because they are not integration objects that can be used to place restrictions on where data may be exported. Option A is incorrect because a stage integration is not a valid type of integration object in Snowflake. A stage is a Snowflake object that references a location where data files are stored, such as an internal stage, an external stage, or a named stage. A stage is not an integration object that provides an interface between Snowflake and third-party services2. Option C is incorrect because a storage integration is a Snowflake object that provides an interface between Snowflake and external cloud storage, such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. A storage integration can be used to securely access data files from external cloud storage without exposing the credentials, but it cannot be used to place restrictions on where data may be exported3. Option D is incorrect because an API integration is a Snowflake object thatprovides an interface between Snowflake and third-party services that use REST APIs, such as Salesforce, Slack, or Twilio. An API integration can be used to securely call external REST APIs from Snowflake using the CALL_EXTERNAL_API function, but it cannot be used to place restrictions on where data may be exported4. References: CREATE SECURITY INTEGRATION | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE STORAGE INTEGRATION | Snowflake Documentation, CREATE API INTEGRATION | Snowflake Documentation

NEW QUESTION # 79
What is the recommended strategy to choose the right sized warehouse to achieve best performance based on query processing?

• A. Run heterogenous queries on the same warehouse
• B. Run homogenous queries on the same warehouse

Answer: B

NEW QUESTION # 80
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. A screen shot of a computer Description automatically generated
  
• B. A computer error message Description automatically generated
  
• C. A close up of text Description automatically generated
  
• D. A black text on a white background Description automatically generated
  

Answer: B

Explanation:
The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:
* Recognizing Disk Spilling
* Managing the Kafka Connector

NEW QUESTION # 81
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) 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
• C. 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
• 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: D

NEW QUESTION # 82
Which Snowflake objects can be used in a data share? (Select TWO).

• A. Stored procedure
• B. Stream
• C. Standard view
• D. External table
• E. Secure view

Answer: C,E

Explanation:
Data sharing is a feature that allows you to share selected objects in a database in your account with other Snowflake accounts. You can share the following Snowflake database objects: external tables, dynamic tables, secure views, secure materialized views, secure UDFs, and tables. However, not all of these objects can be used in a data share. A data share is a named object that encapsulates the information required to share a database. You can grant privileges on objects to a share either via a database role or directly to a share. The objects that can be granted privileges directly to a share are: standard views, secure views, secure UDFs, and tables.
Therefore, the correct answer is A and B. The other options are incorrect because they cannot be granted privileges directly to a share. External tables, dynamic tables, and streams can only be shared via a database role. Stored procedures cannot be shared at all. Reference:
[Introduction to Secure Data Sharing] 1
[Working with Shares] 2
[Choosing How to Share Database Objects] 3

NEW QUESTION # 83
Which statement is true about clustering key?

• A. Clustering key can be many per table and each key can have more than one column
• B. Clustering key can be one per table but the key can have more than one column
• C. Clustering key can be many per table but each key can have only one column

Answer: B

NEW QUESTION # 84
An event table has 150B rows and 1.5M micro-partitions, with the following statistics:
Column NDV*
A_ID 11K
C_DATE 110
NAME 300K
EVENT_ACT_0 1.1G
EVENT_ACT_4 2.2G
*NDV = Number of Distinct Values
What three clustering keys should be used, in order?

• A. C_DATE, A_ID, EVENT_ACT_4
• B. C_DATE, A_ID, NAME
• C. C_DATE, A_ID, EVENT_ACT_0
• D. A_ID, NAME, C_DATE

Answer: C

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:
Clustering keys are most beneficial when they improve micro-partition pruning for common filter patterns and when the chosen columns provide a useful ordering that co-locates data. A common heuristic is to place lower- cardinality columns earlier (to quickly narrow partitions) and then add a higher-cardinality column that further reduces scanned partitions for selective access paths. Here, C_DATE has very low NDV (110), making it an excellent leading key to organize data by date and enable strong pruning for time-bound queries typical of event tables. Next, A_ID (11K) is moderate cardinality and can further segment data within a date range, helping point lookups or narrow scans by identifier. For the third key, the options force choosing between very high-cardinality event activity columns; selecting EVENT_ACT_0 (1.1G) is preferable to EVENT_ACT_4 (2.2G) because it is comparatively less distinct while still supporting additional pruning when queries filter by that attribute. This ordering aligns with Snowflake guidance: keep keys few, ordered to match common predicates, and avoid excessively high-cardinality keys unless they directly match frequent selective filters.
=========

NEW QUESTION # 85
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

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

Answer: C

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. References: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

NEW QUESTION # 86
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between
9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload?
(Choose two.)

• A. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.
• B. Reduce the amount of data that is being processed through this workload.
• C. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.
• D. Set the connection timeout to a higher value than its default.
• E. Increase the size of the virtual warehouse to size X-Large.

Answer: A,C

Explanation:
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
* Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
* Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can
* improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
References:
* Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
* Snowflake Documentation: MAX_CONCURRENCY_LEVEL

NEW QUESTION # 87
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)

• A. Columns can be omitted.
• B. Data can be filtered by an optional where clause.
• C. Incoming data can be joined with other tables.
• D. Columns can be reordered.
• E. The ON ERROR - ABORT statement command can be used.
• F. Type casts are supported.

Answer: A,B,D

Explanation:
The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO <table> statement used by Snowpipe to load data from an ingestion queue into tables1. The statement uses a subquery in the FROM clause to transform the data from the staged files before loading it into the table2.
The transformations supported in the subquery are as follows2:
Data can be filtered by an optional WHERE clause, which specifies a condition that must be satisfied by the rows returned by the subquery. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
wherecol1='A'andcol2>10
);
Columns can be reordered, which means changing the order of the columns in the subquery to match the order of the columns in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selectcol3, col1, col2from@mystage
);
Columns can be omitted, which means excluding some columns from the subquery that are not needed in the target table. For example:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1, col2from@mystage
);
The other options are not supported in the subquery because2:
Type casts are not supported, which means changing the data type of a column in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2)
from(
selectcol1::date, col2from@mystage
);
Incoming data can not be joined with other tables, which means combining the data from the staged files with the data from another table in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable (col1, col2, col3)
from(
selects.col1, s.col2, t.col3from@mystages
joinothertable tons.col1=t.col1
);
The ON ERROR - ABORT statement command can not be used, which means aborting the entire load operation if any error occurs. This command can only be used in the COPY INTO <table> statement, not in the subquery. For example, the following statement will cause an error:
SQLAI-generated code. Review and use carefully. More info on FAQ.
createpipe mypipeas
copyintomytable
from(
select*from@mystage
onerror abort
);
1: CREATE PIPE | Snowflake Documentation
2: Transforming Data During a Load | Snowflake Documentation

NEW QUESTION # 88
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

• A. Choose lower cardinality columns to support clustering keys and cost effectiveness.
• B. Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.
• C. Choose cluster columns that are most actively used in selective filters.
• D. Choose cluster columns that are actively used in the GROUP BY clauses.
• E. Choose columns that are frequently used in join predicates.

Answer: C,E

Explanation:
Explanation
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.
References:
* Clustering Keys & Clustered Tables | Snowflake Documentation
* [Considerations for Choosing Clustering for a Table | Snowflake Documentation]

NEW QUESTION # 89
What are purposes for creating a storage integration? (Choose three.)

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

Answer: A,B,C

Explanation:
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 limitthe 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.
References: : Encryption and Decryption : Private Link for Snowflake : CREATE STORAGE INTEGRATION : Option 1: Configuring a Snowflake Storage Integration to Access Amazon S3

NEW QUESTION # 90
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?

• A. There were JSON nulls in the recent data imports.
• B. The recent data imports contained fewer fields than usual.
• C. There were variations in string lengths for the JSON values in the recent data imports.
• D. The order of the keys in the JSON was changed.

Answer: D

Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
References:
* Considerations for Semi-structured Data Stored in VARIANT
* Snowflake Architect Training
* Snowflake query performance on unique element in variant column
* Snowflake variant performance

NEW QUESTION # 91
What is a valid object hierarchy when building a Snowflake environment?

• A. Account --> Schema > Table --> Stage
• B. Organization --> Account --> Stage --> Table --> View
• C. Organization --> Account --> Database --> Schema --> Stage
• D. Account --> Database --> Schema --> Warehouse

Answer: C

Explanation:
This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:
* Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.
* Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.
* Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.
* Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .
* Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .
The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.
References:
* Snowflake Documentation: Organizations
* Snowflake Blog: Introducing Organizations in Snowflake
* Snowflake Documentation: Accounts
* Snowflake Blog: Understanding Snowflake Account Structures
* Snowflake Documentation: Databases
* Snowflake Blog: How to Create a Database in Snowflake
* [Snowflake Documentation: Schemas]
* [Snowflake Blog: How to Create a Schema in Snowflake]
* [Snowflake Documentation: Stages]
* [Snowflake Blog: How to Use Stages in Snowflake]

NEW QUESTION # 92
Which Snowflake data modeling approach is designed for BI queries?

• A. 3 NF
• B. Data Vault
• C. Snowflake schema
• D. Star schema

Answer: C

NEW QUESTION # 93
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

• A. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
• B. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
• C. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.
• D. A task will move to a suspended state during the daylight savings time change.
• E. A task scheduled in a UTC-based schedule will have no issues with the time changes.

Answer: A,E

Explanation:
According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.
A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.
Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.
Snowflake Documentation: Scheduling Tasks
Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

NEW QUESTION # 94
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