Snowflake ARA-C01 Certification All-in-One Exam Guide May-2024 [Q43-Q59]

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May 19, 2024

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Snowflake ARA-C01 Certification All-in-One Exam Guide May-2024

Get Real ARA-C01 Exam Dumps [May-2024] Practice Tests

NEW QUESTION 43
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?

External table

Materialized view

Search optimization

Result cache

A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
Reference:
Materialized Views | Snowflake Documentation

NEW QUESTION 44
A Snowflake Architect is setting up database replication to support a disaster recovery plan. The primary database has external tables.
How should the database be replicated?

Create a clone of the primary database then replicate the database.

Move the external tables to a database that is not replicated, then replicate the primary database.

Replicate the database ensuring the replicated database is in the same region as the external tables.

Share the primary database with an account in the same region that the database will be replicated to.

Database replication is a feature that allows you to create a copy of a database in another account, region, or cloud platform for disaster recovery or business continuity purposes. However, not all database objects can be replicated. External tables are one of the exceptions, as they reference data files stored in an external stage that is not part of Snowflake. Therefore, to replicate a database that contains external tables, you need to move the external tables to a separate database that is not replicated, and then replicate the primary database that contains the other objects. This way, you can avoid replication errors and ensure consistency between the primary and secondary databases. The other options are incorrect because they either do not address the issue of external tables, or they use an alternative method that is not supported by Snowflake. You cannot create a clone of the primary database and then replicate it, as replication only works on the original database, not on its clones. You also cannot share the primary database with another account, as sharing is a different feature that does not create a copy of the database, but rather grants access to the shared objects. Finally, you do not need to ensure that the replicated database is in the same region as the external tables, as external tables can access data files stored in any region or cloud platform, as long as the stage URL is valid and accessible. References:
* [Replication and Failover/Failback] 1
* [Introduction to External Tables] 2
* [Working with External Tables] 3
* [Replication : How to migrate an account from One Cloud Platform or Region to another in
* Snowflake] 4

NEW QUESTION 45
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company’s Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.

Deploy a Private Data Exchange in combination with data shares for the European accounts.

Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.

Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

Explanation
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
* The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
* The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
* The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
* The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company’s data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. References: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation

NEW QUESTION 46
An Architect needs to design a solution for building environments for development, test, and pre-production, all located in a single Snowflake account. The environments should be based on production data.
Which solution would be MOST cost-effective and performant?

Use zero-copy cloning into transient tables.

Use zero-copy cloning into permanent tables.

Use CREATE TABLE … AS SELECT (CTAS) statements.

Use a Snowflake task to trigger a stored procedure to copy data.

Zero-copy cloning is a feature in Snowflake that allows for the creation of a clone of a database, schema, or table without duplicating any data, which is cost-effective as it saves on storage costs. Transient tables are temporary and do not incur storage costs for the time they are not accessed, making them a cost-effective option for development, test, and pre-production environments that do not require the durability of permanent tables123.
References
*Snowflake Documentation on Zero-Copy Cloning3.
*Articles discussing the cost-effectiveness and performance benefits of zero-copy cloning12.

NEW QUESTION 47
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

They can include ORDER BY clauses.

They cannot include nested subqueries.

They can include context functions, such as CURRENT_TIME().

They can support MIN and MAX aggregates.

They can support inner joins, but not outer joins.

Explanation
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
References:
* Limitations on Creating Materialized Views | Snowflake Documentation
* Working with Materialized Views | Snowflake Documentation

NEW QUESTION 48
Which columns can be included in an external table schema? (Select THREE).

VALUE

METADATASROW_ID

METADATASISUPDATE

METADAT A$ FILENAME

METADATAS FILE_ROW_NUMBER

METADATASEXTERNAL TABLE PARTITION

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 49
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 50
The insertReport endpoint can be thought of like the UNIX command tail

TRUE

FALSE

NEW QUESTION 51
Which of the following are characteristics of Snowflake’s parameter hierarchy?

Session parameters override virtual warehouse parameters.

Virtual warehouse parameters override user parameters.

Table parameters override virtual warehouse parameters.

Schema parameters override account parameters.

In Snowflake’s parameter hierarchy, virtual warehouse parameters take precedence over user parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which typically reflect the requirements of a specific workload or set of queries, override the preferences set at the individual user level.
This helps maintain consistent performance and resource utilization as specified by the administrators managing the virtual warehouses.References: Snowflake documentation on parameter hierarchy, found in the SnowPro Advanced: Architect learning materials.

NEW QUESTION 52
Snowflake data replication can be used to replicate data between cloud providers.

TRUE

FALSE

NEW QUESTION 53
A Snowflake Architect is setting up database replication to support a disaster recovery plan. The primary database has external tables.
How should the database be replicated?

Create a clone of the primary database then replicate the database.

Move the external tables to a database that is not replicated, then replicate the primary database.

Replicate the database ensuring the replicated database is in the same region as the external tables.

Share the primary database with an account in the same region that the database will be replicated to.

NEW QUESTION 54
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?

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.

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.

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.

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.

* 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 55
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

Create accounts for each tenant in the Snowflake organization.

Create an object for each tenant strategy if row level security is viable for isolating tenants.

Create an object for each tenant strategy if row level security is not viable for isolating tenants.

Create a multi-tenant table strategy if row level security is not viable for isolating tenants.

In a scenario where strong legal isolation is required alongside the need for multi-tenancy, the most effective approach is to create separate accounts for each tenant within the Snowflake organization. This approach ensures complete isolation of data, resources, and management, adhering to strict legal and compliance requirements. Role-Based Access Control (RBAC) further enhances security by allowing granular control over who can access what resources within each account. This solution leverages Snowflake’s capabilities for managing multiple accounts under a single organization umbrella, ensuring that each tenant’s data and operations are isolated from others.References: Snowflake documentation on multi-tenancy and account management, part of the SnowPro Advanced: Architect learning path.

NEW QUESTION 56
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.)

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

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

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

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

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

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.

NEW QUESTION 57
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

CSV

XML

Avro

JSON

Parquet

NEW QUESTION 58
Snowflake includes administration settings for resource consumption in order to

Help control costs associated with unexpected credit usage of data

Manage access to data warehouses for specific users

Maintain data availability

NEW QUESTION 59
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?

Loading …

Snowflake ARA-C01 certification is highly respected in the industry and is recognized by many organizations as a standard of excellence in Snowflake architecture and implementation. Achieving this certification demonstrates an individual’s commitment to their profession and their ability to provide high-quality and effective solutions to complex business problems. It also provides a competitive advantage in the job market, as many organizations look for certified Snowflake professionals to lead their data management initiatives.

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