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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Architect Certification |
| Exam Number: | ARA-C01 |
| Passing Score: | 750/1000 |
| Exam Price: | $375 USD |
| Related Certifications: | SnowPro Core Certification |
| Available Languages: | Japanese, English |
| Exam Duration: | 115 minutes |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple Choice, Multiple Select |
| Sample Questions: | Snowflake ARA-C01 Sample Questions |
| Exam Way: | Online proctored or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates must hold an active SnowPro Core Certification. Snowflake recommends 2+ years of hands-on Snowflake architecture experience in production environments. |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/ |
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a professional certification exam offered by Snowflake for individuals who wish to demonstrate advanced knowledge and skills in designing and implementing Snowflake solutions. ARA-C01 exam is the highest-level certification offered by Snowflake and is designed for architects, engineers, and consultants who work with Snowflake on a daily basis.
Snowflake ARA-C01 exam is a vendor-neutral certification that is recognized globally. It is designed to validate the skills and knowledge of professionals in Snowflake architecture and design, making them more competitive in the job market. Passing the exam demonstrates that a candidate has a deep understanding of Snowflake and can design and implement complex Snowflake solutions with high levels of efficiency, scalability, and security. Overall, the Snowflake ARA-C01 Exam is an excellent opportunity for professionals who want to advance their careers in Snowflake architecture and design.
NEW QUESTION # 87
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?
Answer: A,B,D
Explanation:
Explanation
* The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
* CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.
* SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.
* ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.
* Therefore, options A, B, and C are correct.
References: : COPY INTO <table>
NEW QUESTION # 88
An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?
Answer: C
NEW QUESTION # 89
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?
Answer: D
Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
* 1: SnowPro Advanced: Architect | Study Guide 5
* 2: Snowflake Documentation | Query Profile Overview 6
* 3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
* 4: Snowflake Documentation | Optimizing Query Performance 8
* : SnowPro Advanced: Architect | Study Guide
* : Query Profile Overview
* : Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
* : Optimizing Query Performance
NEW QUESTION # 90
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
Answer: B
Explanation:
The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:
* Recognizing Disk Spilling
* Managing the Kafka Connector
NEW QUESTION # 91
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?
Answer: A
NEW QUESTION # 92
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