ARA-C01 Übungstest: SnowPro Advanced Architect Certification & ARA-C01 Braindumps Prüfung

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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Accounts and Security	25%	- Access Control and Authentication 1. Data masking and row access policies 2. Role-based access control 3. SSO and MFA 4. Network policies - Governance and Compliance 1. Data governance strategies 2. Object ownership and privilege management 3. Encryption and security architecture
Topic 2: Performance Optimization	20%	- Cost and Resource Optimization 1. Warehouse cost optimization 2. Concurrency scaling 3. Resource monitors - Query Performance 1. Query profiling and tuning 2. Caching strategies 3. Search optimization service 4. Materialized views
Topic 3: Data Engineering	25%	- Data Loading and Pipelines 1. Streams and CDC 2. External stages and file formats 3. Bulk and continuous data loading 4. Snowpipe and tasks - Data Transformation 1. Data modeling strategies 2. Semi-structured data processing 3. ETL and ELT design
Topic 4: Snowflake Architecture	30%	- Storage and Compute Design 1. Database and schema design 2. Micro-partitions and clustering 3. Warehouse sizing and scaling - Platform Architecture Design 1. Data sharing architecture 2. Multi-account strategies 3. Organization and account structure 4. Cross-region and cross-cloud architecture

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Snowflake SnowPro Advanced Architect Certification ARA-C01 Prüfungsfragen mit Lösungen (Q175-Q180):

175. Frage
How does a standard virtual warehouse policy work in Snowflake?

A. It starts only if the system estimates that there is a query load that will keep the cluster busy for at least
6 minutes.
B. It prevents or minimizes queuing by starting additional clusters instead of conserving credits.
C. It starts only f the system estimates that there is a query load that will keep the cluster busy for at least 2 minutes.
D. It conserves credits by keeping running clusters fully loaded rather than starting additional clusters.

Antwort: B

Begründung:
A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.
Snowflake Documentation: Multi-cluster Warehouses
Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

176. Frage
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?

A. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.
B. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
C. Deploy a Private Data Exchange in combination with data shares for the European accounts.
D. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

Antwort: D

Begründung:
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.

177. Frage
What considerations need to be taken when using database cloning as a tool for data lifecycle management in a development environment? (Select TWO).

A. The clone inherits all granted privileges of all child objects in the source object, including the database.
B. Any pipes in the source referring to external stages are not cloned.
C. The clone inherits all granted privileges of all child objects in the source object, excluding the database.
D. Any pipes in the source are not cloned.
E. Any pipes in the source referring to internal stages are not cloned.

Antwort: B,E

178. Frage
Select the true statements about TASKS

A. TASKS cannot be triggered manually
B. TASKS can be scheduled for SQL execution
C. TASKS can be used with STREAMS
D. TASKS can be used for change data capture

Antwort: A,B,C

179. Frage
An Architect needs to automate the daily Import of two files from an external stage into Snowflake. One file has Parquet-formatted data, the other has CSV-formatted data.
How should the data be joined and aggregated to produce a final result set?

A. Create a materialized view to read, Join, and aggregate the data directly from the external stage, and use the view to produce the final result set
B. Create a JavaScript stored procedure to read. join, and aggregate the data directly from the external stage, and then store the results in a table.
C. Use Snowpipe to ingest the two files, then create a materialized view to produce the final result set.
D. Create a task using Snowflake scripting that will import the files, and then call a User-Defined Function (UDF) to produce the final result set.

Antwort: D

Begründung:
According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. Reference:
Tasks
Snowflake Scripting
User-Defined Functions

180. Frage
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