Get Perfect Results with Premium ARA-C01 Dumps Updated 162 Questions [Q16-Q37]

Get Perfect Results with Premium ARA-C01 Dumps Updated 162 Questions

Free ARA-C01 Exam Study Guide for the NEW Dumps Test Engine

Snowflake ARA-C01 exam covers a wide range of topics, including Snowflake architecture, data loading, performance tuning, security and access control, and data sharing. Candidates are expected to have a deep understanding of Snowflake architecture, including the various components of a Snowflake deployment and how they work together. They must also be able to design and implement Snowflake solutions that meet business requirements and performance expectations.

 

NEW QUESTION # 16
Which copy options are not supported by CREATE PIPE...AS COPY FROM command?

• A. VALIDATION_MODE = RETURN_n_ROWS | RETURN_ERRORS | RETURN_ALL_ERRORS
• B. MATCH_BY_COLUMN_NAME = CASE_SENSITIVE | CASE_INSENSITIVE | NONE
• C. FORCE = TRUE | FALSE
• D. FILES = ( 'file_name1' [ , 'file_name2', ... ] )
• E. ON_ERROR = ABORT_STATEMENT

Answer: A,B,C,D,E

NEW QUESTION # 17
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

• A. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.
• B. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.
• C. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
• D. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.

Answer: D

NEW QUESTION # 18
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

• A. Use, at minimum, the Business Critical edition of Snowflake.
• B. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
• C. Use the External Tokenization feature to obfuscate sensitive data.
• D. Avoid sharing data with partner organizations.
• E. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
• F. Use the Internal Tokenization feature to obfuscate sensitive data.

Answer: A,C,E

NEW QUESTION # 19
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. 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.
• 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. 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.
• D. 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.

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. 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 # 20
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

• A. Data vault
• B. Data lake
• C. lnmon/3NF
• D. Graph model
• E. Dimensional/Kimball
• F. Bayesian hierarchical model

Answer: A,C,E

Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
* Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
* Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2)
* every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
* Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity.
Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.
References: What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 21
A user named USER_01 needs access to create a materialized view on a schema EDW. STG_SCHEMA. How can this access be provided?

• A. GRANT CREATE MATERIALIZED VIEW ON DATABASE EDW TO USER USERJD1;
• B. GRANT CREATE MATERIALIZED VIEW ON SCHEMA EDW.STG_SCHEMA TO USER
  USER_01;
• C. GRANT ROLE NEW_ROLE TO USER_01;
  GRANT CREATE MATERIALIZED VIEW ON EDW.STG_SCHEMA TO NEW_ROLE;
• D. GRANT ROLE NEW_ROLE TO USER USER_01;
  GRANT CREATE MATERIALIZED VIEW ON SCHEMA ECW.STG_SCHEKA TO NEW_ROLE;

Answer: B

Explanation:
* The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
* Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
* Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
* Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:
* Snowflake Documentation: CREATE MATERIALIZED VIEW
* Snowflake Documentation: Working with Materialized Views
* [Snowflake Documentation: GRANT Privileges on a Schema]

NEW QUESTION # 22
An Architect is designing a solution that will be used to process changed records in an orders table.
Newly-inserted orders must be loaded into the f_orders fact table, which will aggregate all the orders by multiple dimensions (time, region, channel, etc.). Existing orders can be updated by the sales department within 30 days after the order creation. In case of an order update, the solution must perform two actions:
1. Update the order in the f_0RDERS fact table.
2. Load the changed order data into the special table ORDER _REPAIRS.
This table is used by the Accounting department once a month. If the order has been changed, the Accounting team needs to know the latest details and perform the necessary actions based on the data in the order_repairs table.
What data processing logic design will be the MOST performant?

• A. Usetwo streams and one task.
• B. Useone stream and two tasks.
• C. Useone stream and one task.
• D. Usetwo streams and two tasks.

Answer: B

Explanation:
The most performant design for processing changed records, considering the need to both update records in the f_orders fact table and load changes into the order_repairs table, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to the f_orders fact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into the order_repairs table, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.References: Snowflake's documentation on streams and tasks for handling data changes efficiently.

NEW QUESTION # 23
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?

• A. Use the Snowflake Connector for Python, connect to remote storage and download the file.
• B. Use the get command in Snowsight to retrieve the file.
• C. Use the get command in SnowSQL to retrieve the file.
• D. Use the Snowflake API endpoint and download the file.

Answer: D

NEW QUESTION # 24
Which of the following are characteristics of Snowflake's parameter hierarchy?

• A. Table parameters override virtual warehouse parameters.
• B. Schema parameters override account parameters.
• C. Session parameters override virtual warehouse parameters.
• D. Virtual warehouse parameters override user parameters.

Answer: C

NEW QUESTION # 25
A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1-> show grants to user user_01;
Command 2 ~> show grants on user user 01;
What inferences can be made about these commands?

• A. Command 1 defines which user owns user_01
  Command 2 defines all the grants which have been given to user_01
• B. Command 1 defines which role owns user_01
  Command 2 defines all the grants which have been given to user_01
• C. Command 1 defines all the grants which are given to user_01Command 2 defines which role owns user 01
• D. Command 1 defines all the grants which are given to user_01 Command 2 defines which user owns user_01

Answer: C

Explanation:
The SHOW GRANTS command in Snowflake can be used to list all the access control privileges that have been explicitly granted to roles, users, and shares. The syntax and the output of the command vary depending on the object type and the grantee type specified in the command1. In this question, the two commands have the following meanings:
* Command 1: show grants to user user_01; This command lists all the roles granted to the user user_01.
The output includes the role name, the grantee name, and the granted by role name for each grant. This command is equivalent to show grants to user current_user if user_01 is the current user1.
* Command 2: show grants on user user_01; This command lists all the privileges that have been granted on the user object user_01. The output includes the privilege name, the grantee name, and the granted by role name for each grant. This command shows which role owns the user object user_01, as the owner role has the privilege to modify or drop the user object2.
Therefore, the correct inference is that command 1 defines all the grants which are given to user_01, and command 2 defines which role owns user_01.
References:
* SHOW GRANTS
* Understanding Access Control in Snowflake

NEW QUESTION # 26
An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

• A. COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;
• B. COPY INTO tablea FROM @%tablea FORCE = TRUE;
• C. COPY INTO tablea FROM @%tablea FILES = ('file5.csv');
• D. COPY INTO tablea FROM @%tablea;
• E. COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;
• F. COPY INTO tablea FROM @%tablea MERGE = TRUE;

Answer: C,D

NEW QUESTION # 27
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

• A. The warehouse MY_WH will be made active every five minutes to check the stream.
• B. The warehouse MY_WH will never suspend.
• C. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
• D. The warehouse MY_WH will only be active when there are results in the stream.

Answer: D

Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
* [CREATE TASK | Snowflake Documentation]
* [Using Streams and Tasks | Snowflake Documentation]
* [CREATE WAREHOUSE | Snowflake Documentation]

NEW QUESTION # 28
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

• A. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• B. Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• C. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector.
  Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
• D. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: D

Explanation:
This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution.
They are also efficient, scalable, and serverless, and they simplify the data transformation process. External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
References:
* Snowpipe Overview | Snowflake Documentation
* Introduction to Data Pipelines | Snowflake Documentation
* External Functions Overview | Snowflake Documentation
* Snowflake Data Marketplace Overview | Snowflake Documentation

NEW QUESTION # 29
An Architect runs the following SQL query:

How can this query be interpreted?

• A. FILERONS is the file format location. FILE_ROW_NUMBER is a stage.
• B. FILEROWS is the table. FILE_ROW_NUMBER is the line number in the table.
• C. FILEROWS is a file. FILE_ROW_NUMBER is the file format location.
• D. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.

Answer: D

Explanation:
A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.
Reference:
: Stages
: LIST Function

NEW QUESTION # 30
Which columns can be included in an external table schema? (Select THREE).

• A. METADATASISUPDATE
• B. METADATASROW_ID
• C. VALUE
• D. METADAT A$ FILENAME
• E. METADATASEXTERNAL TABLE PARTITION
• F. METADATAS FILE_ROW_NUMBER

Answer: C,D,F

Explanation:
An external table schema defines the columns and data types of the data stored in an external stage. All external tables include the following columns by default:
VALUE: A VARIANT type column that represents a single row in the external file.
METADATA$FILENAME: A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.
METADATA$FILE_ROW_NUMBER: A pseudocolumn that shows the row number for each record in a staged data file.
You can also create additional virtual columns as expressions using the VALUE column and/or the pseudocolumns. However, the following columns are not valid for external tables and cannot be included in the schema:
METADATASROW_ID: This column is only available for internal tables and shows the unique identifier for each row in the table.
METADATASISUPDATE: This column is only available for internal tables and shows whether the row was inserted or updated by a merge operation.
METADATASEXTERNAL TABLE PARTITION: This column is not a valid column name and does not exist in Snowflake.

NEW QUESTION # 31
Select the true statement

• A. Privileges are granted to roles. Roles are granted to users
• B. Privileges are granted to both roles and users
• C. Privileges are granted to users. Users are granted to roles

Answer: A

NEW QUESTION # 32
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).

• A. The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.
• B. A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.
• C. A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
• D. All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
• E. An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.

Answer: B,C

Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
* Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2
* Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3
* Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4
* Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
* All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline.
Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
* An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
* The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. References:
* 1: SnowPro Advanced: Architect | Study Guide
* 2: Snowflake Documentation | Creating Stages
* 3: Snowflake Documentation | Loading Data Using Snowpipe
* 4: Snowflake Documentation | Using Streams and Tasks for ELT
* : Snowflake Documentation | Creating Tasks
* : Snowflake Documentation | Best Practices for Loading Data
* : Snowflake Documentation | Using the Snowpipe REST API
* : Snowflake Documentation | Scheduling Tasks
* : SnowPro Advanced: Architect | Study Guide
* : Creating Stages
* : Loading Data Using Snowpipe
* : Using Streams and Tasks for ELT
* : [Creating Tasks]
* : [Best Practices for Loading Data]
* : [Using the Snowpipe REST API]
* : [Scheduling Tasks]

NEW QUESTION # 33
A company has an inbound share set up with eight tables and five secure views. The company plans to make the share part of its production data pipelines.
Which actions can the company take with the inbound share? (Choose two.)

• A. Clone a table from a share.
• B. Create additional views inside the shared database.
• C. Create a table from the shared database.
• D. Create a table stream on the shared table.
• E. Grant modify permissions on the share.

Answer: A,B

Explanation:
Explanation
These two actions are possible with an inbound share, according to the Snowflake documentation and the web search results. An inbound share is a share that is created by another Snowflake account (the provider) and imported into your account (the consumer). An inbound share allows you to access the data shared by the provider, but not to modify or delete it. However, you can perform some actions with the inbound share, such as:
* Clone a table from a share. You can create a copy of a table from an inbound share using the CREATE TABLE ... CLONE statement. The clone will contain the same data and metadata as the original table, but it will be independent of the share. You can modify or delete the clone as you wish, but it will not reflect any changes made to the original table by the provider1.
* Create additional views inside the shared database. You can create views on the tables or views from an inbound share using the CREATE VIEW statement. The views will be stored in the shared database, but they will be owned by your account. You can query the views as you would query any other view in your account, but you cannot modify or delete the underlying objects from the share2.
The other actions listed are not possible with an inbound share, because they would require modifying the share or the shared objects, which are read-only for the consumer. You cannot grant modify permissions on the share, create a table from the shared database, or create a table stream on the shared table34.
References:
* Cloning Objects from a Share | Snowflake Documentation
* Creating Views on Shared Data | Snowflake Documentation
* Importing Data from a Share | Snowflake Documentation
* Streams on Shared Tables | Snowflake Documentation

NEW QUESTION # 34
A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.
How can these requirements be met?

• A. Use on error = SKIP_FILE in the copy into command.
• B. Use ON_ERROR = continue in the copy into command.
• C. Use FURGE = FALSE in the copy into command.
• D. Use purge = TRUE in the copy into command.

Answer: A

Explanation:
For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.

NEW QUESTION # 35
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 and no users will be able to see the unmasked data.
• B. Masked data will be loaded into the new column.
• C. Unmasked data will be loaded in the new column.
• D. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.

Answer: C

NEW QUESTION # 36
What are purposes for creating a storage integration? (Choose three.)

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

Answer: A,C,D

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 # 37
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