ARA-C01자격증공부 - ARA-C01시험대비자료

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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Sharing and Collaboration	- Secure data sharing 1. Cross-account data sharing 2. Data marketplace concepts
Topic 2: Performance and Optimization	- Cost optimization 1. Storage and compute efficiency 2. Resource monitoring and cost control - Query performance tuning 1. Warehouse sizing and scaling 2. Query profiling and optimization techniques
Topic 3: Security, Governance, and Compliance	- Data governance 1. Audit and compliance features 2. Data classification and masking policies - Access control and security model 1. Network security and authentication 2. Role-based access control (RBAC)
Topic 4: Data Architecture and Design	- Data modeling and schema design 1. Semi-structured data modeling 2. Star and snowflake schema concepts - Design scalable Snowflake architectures 1. Multi-cluster virtual warehouse design 2. Account and environment strategy
Topic 5: Data Pipelines and Data Engineering	- Data ingestion and transformation 1. Batch and streaming ingestion 2. Snowpipe and continuous data loading - Data orchestration 1. Tasks and Streams usage 2. Pipeline design patterns

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ARA-C01시험대비자료 - ARA-C01최신 시험덤프공부자료

Fast2test 의 IT전문가들이 자신만의 경험과 끊임없는 노력으로 최고의 Snowflake ARA-C01학습자료를 작성해 여러분들이Snowflake ARA-C01시험에서 패스하도록 최선을 다하고 있습니다. 덤프는 최신 시험문제를 커버하고 있어 시험패스율이 높습니다. Snowflake ARA-C01시험을 보기로 결심한 분은 가장 안전하고 가장 최신인 적중율 100%에 달하는Snowflake ARA-C01시험대비덤프를 Fast2test에서 받을 수 있습니다.

최신 SnowPro Advanced Certification ARA-C01 무료샘플문제 (Q58-Q63):

질문 # 58
Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0),
CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME
VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE VARCHAR(20) ); ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID); Which queries will benefit from the use of the search optimization service? (Select TWO).

A. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDNOT LIKE
'%AAAAAAAAPHPPL%';
B. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE
'%BAAASKD%';
C. select*fromDEMO_DB.DEMO_SCH.CUSTOMER_ADDRESSWhereCA_ADDRESS_IDLIKE
'%PHPP%';
D. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where
substring(CA_ADDRESS_ID,1,8)=
substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,8);
E. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,16);

정답：D,E

설명：
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.

질문 # 59
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?

A. 1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
5. Give the materialized views access to the dashboard tool.
B. 1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
4. Give the transformed table access to the dashboard tool.
5. Perform the aggregations on the dashboard tool.
C. 1. Create a Snowpipe to bring the JSON data into Snowflake.
2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
4. Join the vendor's data back to the loT data in a transformation procedure
5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the materialized views access to the dashboard tool.
D. 1. Create an external table over the JSON data in cloud storage.
2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
4. Join the vendor's data back to the loT data using a transformation procedure.
5. Create views over the larger dataset to perform the aggregations required by the dashboard.
6. Give the views access to the dashboard tool.

정답：C

설명：
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
Reference =
* Snowflake Documentation on Snowpipe4
* Snowflake Documentation on Secure Data Sharing2
* Best Practices for Data Ingestion with Snowflake1

질문 # 60
An Architect is designing a data lake with Snowflake. The company has structured, semi-structured, and unstructured data. The company wants to save the data inside the data lake within the Snowflake system. The company is planning on sharing data among its corporate branches using Snowflake data sharing.
What should be considered when sharing the unstructured data within Snowflake?

A. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with the "expiration_time" argument defined for the URL time limit.
B. A pre-signed URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with no time limit for the URL.
C. A scoped URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 24-hour time limit for the URL.
D. A file URL should be used to save the unstructured data into Snowflake in order to share data over secure views, with a 7-day time limit for the URL.

정답：C

설명：
When sharing unstructured data within Snowflake, using a scoped URL is recommended. Scoped URLs provide temporary access to staged files without granting privileges to the stage itself, enhancing security. The URL expires when the persisted query result period ends, which is currently set to 24 hours. This approach is suitable for sharing unstructured data over secure views within Snowflake's data sharing framework.

질문 # 61
The following chart represents the performance of a virtual warehouse over time:

A Data Engineer notices that the warehouse is queueing queries. The warehouse is sizeX-Small, theminimum and maximum cluster counts are set to 1, thescaling policy is set to standard, andauto-suspend is set to 10 minutes.
How can the performance be improved?

A. Change auto-suspend to a longer time frame.
B. Change the cluster settings.
C. Change the scaling policy to economy.
D. Increase the size of the warehouse.

정답：B

질문 # 62
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.)

A. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
B. Create a set of profile-specific databases that aligns data with usage patterns.
C. Create a raw database for landing and persisting raw data entering the data pipelines.
D. Consolidate data in the company's data lake and use EXTERNAL TABLES.
E. Create a single star schema in a single database to support all consumers' requirements.

정답：B,C

설명：
These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
References:
* Snowflake Data Lake Architecture | Snowflake Documentation
* Snowflake Data Lake Best Practices | Snowflake Documentation

질문 # 63
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ARA-C01시험대비자료: https://kr.fast2test.com/ARA-C01-premium-file.html

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