[Jun-2024] SnowPro Advanced: Architect ARA-R01 Exam Practice Dumps [Q51-Q65]

NEW QUESTION 51
An Architect is using SnowCD to investigate a connectivity issue.
Which system function will provide a list of endpoints that the network must be able to access to use a specific Snowflake account, leveraging private connectivity?

SYSTEMSALLOWLIST ()

SYSTEMSGET_PRIVATELINK

SYSTEMSAUTHORIZE_PRIVATELINK

SYSTEMSALLOWLIST_PRIVATELINK ()

NEW QUESTION 52
What are purposes for creating a storage integration? (Choose three.)

Control access to Snowflake data using a master encryption key that is maintained in the cloud provider’s key management service.

Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.

Support multiple external stages using one single Snowflake object.

Avoid supplying credentials when creating a stage or when loading or unloading data.

Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.

Manage credentials from multiple cloud providers in one single Snowflake object.

NEW QUESTION 53
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

Table T_SALES_CLONE successfully created.

Time Travel data is not available for table T_SALES.

The offset -> is not a valid clause in the clone operation.

Syntax error line 1 at position 58 unexpected ‘at’.

NEW QUESTION 54
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?

A screen shot of a computer Description automatically generated

A white background with black text Description automatically generated

A close up of a message Description automatically generated

A multi-cluster warehouse is a virtual warehouse that can scale compute resources by adding or removing clusters based on the workload demand. A multi-cluster warehouse can improve the performance of a particular workload by reducing the query queue time and the data spillage to local storage. To identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse, you need to analyze the query history and look for the following indicators:
High average queued load: This metric shows the average number of queries waiting in the queue for each warehouse cluster. A high value indicates that the warehouse is overloaded and cannot handle the concurrency demand.
High bytes spilled to local storage: This metric shows the amount of data that was spilled from memory to local disk during query processing. A high value indicates that the warehouse size is too small and cannot fit the data in memory.
High variation in workload: This metric shows the fluctuation in the number of queries submitted to the warehouse over time. A high variation indicates that the workload is unpredictable and dynamic, and requires a flexible scaling policy.
The query in option C is the best one to identify these indicators, as it selects the date, warehouse name, bytes spilled to local storage, and sum of average queued load from the query history table, and filters the results where bytes spilled to local storage is greater than zero. This query will show the days and warehouses that experienced data spillage and high queue time, and could benefit from a multi-cluster warehouse with auto-scale mode.
The query in option A is not correct, as it only selects the date and warehouse name, and does not include any metrics to measure the performance of the workload. The query in option B is not correct, as it selects the date, warehouse name, and average execution time, which is not a good indicator of the need for a multi-cluster warehouse. The query in option D is not correct, as it selects the date, warehouse name, and average credits used, which is not a good indicator of the need for a multi-cluster warehouse either.
References: Multi-cluster Warehouses, Query History View, Reducing Queues

NEW QUESTION 55
Company A would like to share data in Snowflake with Company B.
Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

Create a pipeline to write shared data to a cloud storage location in the target cloud provider.

Ensure that all views are persisted, as views cannot be shared across cloud platforms.

Setup data replication to the region and cloud platform where the consumer resides.

Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer’s account. The replica database is read-only and automatically synchronized with the primary database in the provider’s account. Data replication is useful for scenarios where data sharing is not possible ordesirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer’s account, and it would not leverage the benefits of Snowflake’s data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is not required for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: Replicating Databases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation

NEW QUESTION 56
What Snowflake features should be leveraged when modeling using Data Vault?

Snowflake’s support of multi-table inserts into the data model’s Data Vault tables

Data needs to be pre-partitioned to obtain a superior data access performance

Scaling up the virtual warehouses will support parallel processing of new source loads

Snowflake’s ability to hash keys so that hash key joins can run faster than integer joins

NEW QUESTION 57
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

Task schedules can be designed to follow specified or local time zones to accommodate the time changes.

A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.

A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.

A task scheduled in a UTC-based schedule will have no issues with the time changes.

A task will move to a suspended state during the daylight savings time change.

NEW QUESTION 58
A company has an external vendor who puts data into Google Cloud Storage. The company’s Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?

Ask the vendor to create a Snowflake account, load the data into Snowflake and create a data share.

Create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake.

Copy the data from Google Cloud Storage to Azure Blob storage using external tools and load data from Blob storage to Snowflake.

Create a Snowflake Account in the Google Cloud Platform (GCP), ingest data into this account and use data replication to move the data from GCP to Azure.

NEW QUESTION 59
A company has a Snowflake account named ACCOUNTA in AWS us-east-1 region. The company stores its marketing data in a Snowflake database named MARKET_DB. One of the company’s business partners has an account named PARTNERB in Azure East US 2 region. For marketing purposes the company has agreed to share the database MARKET_DB with the partner account.
Which of the following steps MUST be performed for the account PARTNERB to consume data from the MARKET_DB database?

Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA create a share of database MARKET_DB, create a new database out of this share locally in AWS us-east-1 region, and replicate this new database to AZABC123 account. Then set up data sharing to the PARTNERB account.

From account ACCOUNTA create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then make this database the provider and share it with the PARTNERB account.

Create a new account (called AZABC123) in Azure East US 2 region. From account ACCOUNTA replicate the database MARKET_DB to AZABC123 and from this account set up the data sharing to the PARTNERB account.

Create a share of database MARKET_DB, and create a new database out of this share locally in AWS us-east-1 region. Then replicate this database to the partner’s account PARTNERB.

NEW QUESTION 60
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE … AS COPY … FROM (…)

Data can be filtered by an optional where clause.

Columns can be reordered.

Columns can be omitted.

Type casts are supported.

Incoming data can be joined with other tables.

The ON ERROR – ABORT statement command can be used.

NEW QUESTION 61
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

The MERGE command

The UPSERT command

The CHANGES clause

A STREAM object

The CHANGE_DATA_CAPTURE command

NEW QUESTION 62
A Snowflake Architect created a new data share and would like to verify that only specific records in secure views are visible within the data share by the consumers.
What is the recommended way to validate data accessibility by the consumers?

Create reader accounts as shown below and impersonate the consumers by logging in with their credentials.
create managed account reader_acctl admin_name = userl , adroin_password ‘Sdfed43da!44T , type = reader;

Create a row access policy as shown below and assign it to the data share.
create or replace row access policy rap_acct as (acct_id varchar) returns boolean -> case when
‘acctl_role’ = current_role() then true else false end;

Set the session parameter called SIMULATED_DATA_SHARING_C0NSUMER as shown below in order to impersonate the consumer accounts.
alter session set simulated_data_sharing_consumer – ‘Consumer Acctl*

Alter the share settings as shown below, in order to impersonate a specific consumer account.
alter share sales share set accounts = ‘Consumerl’ share restrictions = true

NEW QUESTION 63
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?

The warehouse MY_WH will be made active every five minutes to check the stream.

The warehouse MY_WH will only be active when there are results in the stream.

The warehouse MY_WH will never suspend.

The warehouse MY_WH will automatically resize to accommodate the size of the stream.

NEW QUESTION 64
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

Choose columns that are frequently used in join predicates.

Choose lower cardinality columns to support clustering keys and cost effectiveness.

Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.

Choose cluster columns that are most actively used in selective filters.

Choose cluster columns that are actively used in the GROUP BY clauses.

NEW QUESTION 65
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?

Use ON_ERROR = continue in the copy into command.

Use purge = TRUE in the copy into command.

Use FURGE = FALSE in the copy into command.

Use on error = SKIP_FILE in the copy into command.