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[General] 100% Pass Quiz SOL-C01 - Reliable Valid Test Snowflake Certified SnowPro Associa

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【General】 100% Pass Quiz SOL-C01 - Reliable Valid Test Snowflake Certified SnowPro Associa

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Snowflake SOL-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
Topic 2
  • Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
Topic 3
  • Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
Topic 4
  • Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.

Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q108-Q113):NEW QUESTION # 108
What does "warehouse scaling in/out" refer to in Snowflake?
  • A. Changing the region of the warehouse.
  • B. Adjusting the number of clusters in a multi-cluster warehouse.
  • C. Changing the size of the warehouse (e.g., from Small to Medium or vice versa).
  • D. Moving data between different storage locations.
Answer: B
Explanation:
Scalingin/outin Snowflake refers to modifying thenumber of compute clustersassociated with a multi-cluster virtual warehouse. Scalingoutincreases cluster count to accommodate higher concurrency or workload spikes, allowing more queries to run simultaneously without queuing. Scalinginreduces cluster count during periods of lower demand, optimizing compute usage and costs. This is distinct fromscaling up/down, which refers to changing warehouse size (e.g., Small, Medium). Scaling does not involve data movement or region changes; warehouse compute is stateless and operates independently of storage. Multi-cluster warehouses allow Snowflake to automatically add or remove clusters based on demand when auto-scale policies are configured.

NEW QUESTION # 109
You are tasked with creating a table `EMPLOYEES in Snowflake to store employee data. The table should have columns for 'employee_id' (INT, primary key), 'first_name' (VARCHAR(50)),
'last_name' (VARCHAR(50)), 'email' (VARCHAR(IOO)), and 'hire_date' (DATE). You want to ensure that when loading data, any rows with duplicate 'employee_id' values are rejected without failing the entire load . Furthermore, you need to automatically generate surrogate keys for any new departments added to the 'DEPARTMENTS' table, which is not currently populated but will be loaded later. Which of the following approaches correctly combines these requirements?
  • A. Create the 'EMPLOYEES' table with 'employee_id' as the primary key. Use a COPY INTO statement with the 'ON_ERROR = SKIP_FILE option. Use a SEQUENCE object and a DEFAULT constraint to generate department keys.
  • B. Create the 'EMPLOYEES table with a unique constraint on 'employee_id' and use a COPY INTO statement with the 'ON_ERROR = SKIP_FILE' option. Use a SEQUENCE object and a DEFAULT constraint to generate department keys.
  • C. Create the `EMPLOYEES table with a unique constraint on 'employee_id'. IJse a COPY INTO statement with the 'ON ERROR = SKIP FILE option and VALIDATION_MODE = RETURN_ERRORS. Use an IDENTITY column for department keys.
  • D. Create the 'EMPLOYEES' table without a primary key constraint. Use a COPY INTO statement with the 'ON_ERROR = ABORT_STATEMENT option. Use a Snowflake Task to periodically check for new departments and generate keys.
  • E. Create the 'EMPLOYEES' table with 'employee_id' as the primary key. Use a COPY INTO statement with the 'ON_ERROR = CONTINUE option. Use an IDENTITY column for department keys.
Answer: C
Explanation:
Option E correctly addresses both requirements. Creating a unique constraint on allows Snowflake to identify duplicate rows. The = SKIP FILE' option, combined with
'VALIDATION_MODE = RETURN ERRORS', ensures that duplicate rows will not be inserted, and the COPY INTO operation will continue. Using an IDENTITY column for "DEPARTMENTS' simplifies the automatic generation of surrogate keys. Options A, B, and C will not properly handle the error situation and may stop the load completely. Option D doesn't prevent duplicates and the Snowflake Task isn't the simplest approach.

NEW QUESTION # 110
You are loading data into a Snowflake table using the 'COPY INTO' command. The data is in JSON format, and one of the fields in the JSON data, 'timestamp_field', represents a Unix epoch timestamp in seconds. You need to convert this timestamp to a Snowflake timestamp data type during the loading process. Which of the following 'COPY INTO' statements would correctly perform this conversion?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: A
Explanation:
The correct solution uses 'TO_TIMESTAMP' with the result of , 'timestamp_field')' cast to NUMBER (since epoch time is a number). `GET PATH' returns a VARIANT, which needs to be explicitly cast to the correct data type before being passed to TO_TIMESTAMP. The cast converts the VARIANT to a numeric value. The other options either don't correctly cast the VARIANT or use an incorrect TO TIMESTAMP function variation.

NEW QUESTION # 111
What Snowflake object is used to organize database objects into logical groupings?
  • A. Stages
  • B. Tables
  • C. Schemas
  • D. Roles
Answer: C
Explanation:
Schemas serve as logical containers within a database, grouping tables, views, file formats, functions, and other objects.
Roles control access, not organization.
Tables store data but do not group objects.
Stages store files or point to external storage; they are not organizational containers.

NEW QUESTION # 112
A data engineering team is experiencing performance issues with their nightly ETL pipeline in Snowflake. The pipeline involves complex transformations on a large dataset (5TB) and is executed within a single Snowflake virtual warehouse (size: Large). The team notices that the warehouse is frequently hitting resource limits (CPU and Memory) during peak processing times, even though the overall execution time is only 2 hours. Which of the following strategies would BEST address the performance bottleneck and optimize resource utilization, considering cost- effectiveness?
  • A. Break down the ETL pipeline into smaller, independent tasks and use multiple smaller virtual warehouses (size: Medium) to execute them in parallel. This utilizes Snowflake's multi-cluster architecture and distributes the workload.
  • B. Migrate the entire ETL pipeline to a different data processing platform like Apache Spark, as Snowflake is not suitable for complex transformations on large datasets.
  • C. Implement scaling policies for the virtual warehouse. Configure it to automatically scale up to X- Large during the ETL pipeline's execution and then scale back down to Large when the pipeline completes.
  • D. Optimize the SQL queries within the ETL pipeline by identifying and rewriting inefficient queries, adding appropriate indexes, and leveraging Snowflake's query optimization features.
  • E. Upgrade the virtual warehouse size to X-Large to provide more CPU and memory resources. This directly addresses the resource contention.
Answer: A,D
Explanation:
Option C (Breaking down the ETL pipeline) is a strong choice as it leverages Snowflake's multi- cluster architecture for parallel processing, improving performance and resource utilization.
Option D (Optimizing SQL queries) is also crucial. Inefficient queries can significantly impact performance. Options A and B address the problem, but not as efficiently as C and D. While upgrading the warehouse (A) might provide temporary relief, it doesn't fundamentally address inefficiencies. Auto-scaling (B) is good, but splitting the load provides true parallelism. Option E is an extreme measure and likely unnecessary with proper optimization.

NEW QUESTION # 113
......
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