• A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
• One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
• Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user’s role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
• A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
• Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
• Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user’s role without modifying the queries3.
• Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.

References: : Snowflake’s Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing

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]

The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.

• Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.
• Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval.Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches.References: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.

• Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.
• Among the objects listed in the question, the following ones can be cloned in Snowflake:
• The following objects listed in the question cannot be cloned in Snowflake:

References: : Cloning Considerations : CREATE TABLE … CLONE : CREATE EXTERNAL TABLE … CLONE : Temporary Tables : Internal Stages

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.

According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:

• Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
• Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
• Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.

The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrect because shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

Data Vault 2.0 on Snowflake supports the HASH_DIFF concept for change data capture, which is a method to detect changes in the data by comparing the hash values of the records. Additionally, Snowflake’s multi-table insert feature allows for the loading of multiple PIT tables in parallel from a single join query, which can significantly streamline the data loading process and improve performance1.

References =

•Snowflake’s documentation on multi-table inserts1

•Blog post on optimizing Data Vault architecture on Snowflake2

In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.References: Snowflake Documentation on Result Set Caching.

In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.References: Snowflake Documentation on MERGE and STREAM Objects.

• The SQL statement is a command for creating a pipe in Snowflake, which is an object that defines the COPY INTO 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:

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

  create pipe mypipe as

  copy into mytable

  from (

  select * from @mystage

  where col1 = 'A' and col2 > 10

  );

  • uk.co.certification.simulator.questionpool.PList@1b895f20

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

  create pipe mypipe as

  copy into mytable (col1, col2, col3)

  from (

  select col3, col1, col2 from @mystage

  );

  • uk.co.certification.simulator.questionpool.PList@1b8960a0

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

  create pipe mypipe as

  copy into mytable (col1, col2)

  from (

  select col1, col2 from @mystage

  );

  • The other options are not supported in the subquery because2:

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

  create pipe mypipe as

  copy into mytable (col1, col2)

  from (

  select col1::date, col2 from @mystage

  );

  • uk.co.certification.simulator.questionpool.PList@1b896810

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

  create pipe mypipe as

  copy into mytable (col1, col2, col3)

  from (

  select s.col1, s.col2, t.col3 from @mystage s

  join othertable t on s.col1 = t.col1

  );

  • uk.co.certification.simulator.questionpool.PList@1b896a00

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

  create pipe mypipe as

  copy into mytable

  from (

  select * from @mystage

  on error abort

  );

  References:

  • 1: CREATE PIPE | Snowflake Documentation
  • 2: Transforming Data During a Load | Snowflake Documentation
  Questions 36

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

  

  This command results in the following error:

  SQL compilation error: invalid parameter 'validation_mode'

  Assuming the syntax is correct, what is the cause of this error?

  Options:

  A.  

  The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.

  B.  

  The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.

  C.  

  The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.

  D.  

  The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.

  Suggested Solution Discussion 0
  Questions 37

  An Architect entered the following commands in sequence:

  

  USER1 cannot find the table.

  Which of the following commands does the Architect need to run for USER1 to find the tables using the Principle of Least Privilege? (Choose two.)

  Options:

  A.  

  GRANT ROLE PUBLIC TO ROLE INTERN;

  B.  

  GRANT USAGE ON DATABASE SANDBOX TO ROLE INTERN;

  C.  

  GRANT USAGE ON SCHEMA SANDBOX.PUBLIC TO ROLE INTERN;

  D.  

  GRANT OWNERSHIP ON DATABASE SANDBOX TO USER INTERN;

  E.  

  GRANT ALL PRIVILEGES ON DATABASE SANDBOX TO ROLE INTERN;

  Suggested Solution Discussion 0

  Answer:

  B, C

  Explanation:

  Explanation:

  • According to the Principle of Least Privilege, the Architect should grant the minimum privileges necessary for the USER1 to find the tables in the SANDBOX database.
  • The USER1 needs to have USAGE privilege on the SANDBOX database and the SANDBOX.PUBLIC schema to be able to access the tables in the PUBLIC schema. Therefore, the commands B and C are the correct ones to run.
  • The command A is not correct because the PUBLIC role is automatically granted to every user and role in the account, and it does not have any privileges on the SANDBOX database by default.
  • The command D is not correct because it would transfer the ownership of the SANDBOX database from the Architect to the USER1, which is not necessary and violates the Principle of Least Privilege.
  • The command E is not correct because it would grant all the possible privileges on the SANDBOX database to the USER1, which is also not necessary and violates the Principle of Least Privilege.

  References: : Snowflake - Principle of Least Privilege : Snowflake - Access Control Privileges : Snowflake - Public Role : Snowflake - Ownership and Grants

  Questions 38

  An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.

  Based on this scenario, what Time Travel query options are available on the secondary database?

  Options:

  A.  

  A query using Time Travel in the secondary database is available for every hourly table version within the retention window.

  B.  

  A query using Time Travel in the secondary database is available for every hourly table version within and outside the retention window.

  C.  

  Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) in the retention window.

  D.  

  Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) and outside the retention window.

  Suggested Solution Discussion 0
  Questions 39

  What are characteristics of Dynamic Data Masking? (Select TWO).

  Options:

  A.  

  A masking policy that Is currently set on a table can be dropped.

  B.  

  A single masking policy can be applied to columns in different tables.

  C.  

  A masking policy can be applied to the value column of an external table.

  D.  

  The role that creates the masking policy will always see unmasked data In query results

  E.  

  A masking policy can be applied to a column with the GEOGRAPHY data type.

  Suggested Solution Discussion 0

  Answer:

  A, B

  Explanation:

  Explanation:

  Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. References: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

  Questions 40

  The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:

  1) Finance and Vendor Management team members who require reporting and visualization

  2) Data Science team members who require access to raw data for ML model development

  3) Sales team members who require engineered and protected data for data monetization

  What Snowflake data modeling approaches will meet these requirements? (Choose two.)

  Options:

  A.  

  Consolidate data in the company’s data lake and use EXTERNAL TABLES.

  B.  

  Create a raw database for landing and persisting raw data entering the data pipelines.

  C.  

  Create a set of profile-specific databases that aligns data with usage patterns.

  D.  

  Create a single star schema in a single database to support all consumers’ requirements.

  E.  

  Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

  Suggested Solution Discussion 0

  Answer:

  B, C

  Explanation:

  Explanation:

  To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.

  Option B: By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.

  Option C: Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.

  Questions 41

  Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

  

  Sample data for the CREDITCARDINFO table is as follows:

  NAME EXPIRYDATE CREDITCARDNO

  JOHN DOE 2022-07-23 4321 5678 9012 1234

  if the Snowflake system rotes have not been granted any additional roles, what will be the result?

  Options:

  A.  

  The sysadmin can see the CREDICARDND column data in clear text.

  B.  

  The owner of the table will see the CREDICARDND column data in clear text.

  C.  

  Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.

  D.  

  Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.

  Suggested Solution Discussion 0
  Questions 42

  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?

  Options:

  A.  

  The timestamps are different because there are parameter setup mismatches. The parameters need to be realigned

  B.  

  The Snowflake timezone parameter Is different from the cloud provider's parameters causing the mismatch.

  C.  

  The Table Designer team has not used the localtimestamp or systimestamp functions in the Snowflake copy statement.

  D.  

  The CURRENT_TIMEis evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table.

  Suggested Solution Discussion 0
  Questions 43

  A company wants to Integrate its main enterprise identity provider with federated authentication with Snowflake.

  The authentication integration has been configured and roles have been created in Snowflake. However, the users are not automatically appearing in Snowflake when created and their group membership is not reflected in their assigned rotes.

  How can the missing functionality be enabled with the LEAST amount of operational overhead?

  Options:

  A.  

  OAuth must be configured between the identity provider and Snowflake. Then the authorization server must be configured with the right mapping of users and roles.

  B.  

  OAuth must be configured between the identity provider and Snowflake. Then the authorization server must be configured with the right mapping of users, and the resource server must be configured with the right mapping of role assignment.

  C.  

  SCIM must be enabled between the identity provider and Snowflake. Once both are synchronized through SCIM, their groups will get created as group accounts in Snowflake and the proper roles can be granted.

  D.  

  SCIM must be enabled between the identity provider and Snowflake. Once both are synchronized through SCIM. users will automatically get created and their group membership will be reflected as roles In Snowflake.

  Suggested Solution Discussion 0
  Questions 44

  A healthcare company wants to share data with a medical institute. The institute is running a Standard edition of Snowflake; the healthcare company is running a Business Critical edition.

  How can this data be shared?

  Options:

  A.  

  The healthcare company will need to change the institute’s Snowflake edition in the accounts panel.

  B.  

  By default, sharing is supported from a Business Critical Snowflake edition to a Standard edition.

  C.  

  Contact Snowflake and they will execute the share request for the healthcare company.

  D.  

  Set the share_restriction parameter on the shared object to false.

  Suggested Solution Discussion 0
  Questions 45

  An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports.

  The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters:

  AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium

  What is the MOST cost-effective way to increase the availability of the reports?

  Options:

  A.  

  Use materialized views and pre-calculate the data.

  B.  

  Increase the warehouse to size Large and set auto_suspend = 600.

  C.  

  Use a multi-cluster warehouse in maximized mode with 2 size Medium clusters.

  D.  

  Use a multi-cluster warehouse in auto-scale mode with 1 size Medium cluster, and set min_cluster_count = 1 and max_cluster_count = 4.

  Suggested Solution Discussion 0
  Questions 46

  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.)

  Options:

  A.  

  COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;

  B.  

  COPY INTO tablea FROM @%tablea;

  C.  

  COPY INTO tablea FROM @%tablea FILES = ('file5.csv');

  D.  

  COPY INTO tablea FROM @%tablea FORCE = TRUE;

  E.  

  COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;

  F.  

  COPY INTO tablea FROM @%tablea MERGE = TRUE;

  Suggested Solution Discussion 0

  Answer:

  B, C

  Explanation:

  Explanation:

  • Option A (RETURN_FAILED_ONLY) will only load files that previously failed to load. Since file5.csv already exists in the stage with the same name, it will not be considered a new file and will not be loaded.
  • Option D (FORCE) will overwrite any existing data in the table. This is not desired as we only want to load the data from file5.csv.
  • Option E (NEW_FILES_ONLY) will only load files that have been added to the stage since the last COPY command. This will not work because file5.csv was already in the stage before it was fixed.
  • Option F (MERGE) is used to merge data from a stage into an existing table, creating new rows for any data not already present. This is not needed in this case as we simply want to load the data from file5.csv.

  Therefore, the architect can use either COPY INTO tablea FROM @%tablea or COPY INTO tablea FROM @%tablea FILES = ('file5.csv') to load only file5.csv from the stage. Both options will load the data from the specified file without overwriting any existing data or requiring additional configuration

  Questions 47

  What are characteristics of the use of transactions in Snowflake? (Select TWO).

  Options:

  A.  

  Explicit transactions can contain DDL, DML, and query statements.

  B.  

  The autocommit setting can be changed inside a stored procedure.

  C.  

  A transaction can be started explicitly by executing a begin work statement and end explicitly by executing a commit work statement.

  D.  

  A transaction can be started explicitly by executing a begin transaction statement and end explicitly by executing an end transaction statement.

  E.  

  Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.

  Suggested Solution Discussion 0

  Answer:

  A, C

  Explanation:

  Explanation:

  A. Snowflake's transactions can indeed include DDL (Data Definition Language), DML (Data Manipulation Language), and query statements. When executed within a transaction block, they all contribute to the atomicity of the transaction—either all of them commit together or none at all.C. Snowflake supports explicit transaction control through the use of the BEGIN TRANSACTION (or simply BEGIN) and COMMIT statements. Alternatively, the BEGIN WORK and COMMIT WORK syntax is also supported, which is a standard SQL syntax for initiating and ending transactions, respectively.Note: The END TRANSACTION statement is not used in Snowflake to end a transaction; the correct statement is COMMIT or COMMIT WORK.

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