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[Hardware] Exam SOL-C01 Simulations, SOL-C01 Training Online

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【Hardware】 Exam SOL-C01 Simulations, SOL-C01 Training Online

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q15-Q20):NEW QUESTION # 15
What does a "green" cell execution status indicate in Snowflake Notebooks?
  • A. Execution failed
  • B. Execution was canceled
  • C. Execution is pending
  • D. Execution succeeded
Answer: D

NEW QUESTION # 16
Which options are used in Snowflake notebooks for querying data? (Choose any 3 options)
  • A. SQL
  • B. Python
  • C. Markup
  • D. Markdown
Answer: A,B,D
Explanation:
Snowflake Notebooks support multiple cell types for development and documentation.Pythoncells allow users to perform data transformations, analysis, and Snowpark operations.SQLcells enable executing Snowflake SQL queries directly against tables and views.Markdowncells support formatted documentation within notebooks-useful for narrative explanations and commentary. The option "Markup" is not a supported cell type in Snowflake Notebooks. These three supported types allow users to blend analytics, code execution, and documentation in a unified environment.

NEW QUESTION # 17
A data warehouse contains a table 'ORDERS' with columns 'ORDER ID', 'CUSTOMER ID',
'ORDER DATE, and `ORDER TOTAL' You need to optimize a query that frequently retrieves the total order amount for a specific customer within a given date range. Which of the following strategies would be MOST effective in improving query performance?
  • A. create an index on the 'ORDER DATE column.
  • B. Increase the warehouse size to allow for more compute resources.
  • C. Create a view that filters the `ORDERS' table based on the date range.
  • D. Cluster the `ORDERS' table using `CUSTOMER ID and `ORDER DATES.
  • E. Create a materialized view that pre-calculates the total order amount for each customer.
Answer: D
Explanation:
Clustering the table on `CUSTOMER ID and 'ORDER DATE is the most effective strategy because it physically organizes the data on disk based on these columns. This allows Snowflake to efficiently retrieve the relevant data for a specific customer and date range, minimizing the amount of data that needs to be scanned. Increasing warehouse size will improve general performance, but clustering optimizes for this specific query. Indexes are not typically used in Snowflake. A materialized view would be good, however, clustering is more effective.

NEW QUESTION # 18
A Snowflake data engineer is tasked with optimizing the performance of daily data loads into a fact table named `SALES FACT. The data is loaded from multiple CSV files stored in an AWS S3 bucket. The data engineer observes that the data loading process is slow, even though the virtual warehouse is adequately sized. Analyzing the COPY INTO command, they notice that data loading happens in single thread even though multiple files are staged. Which of the following techniques, or combination of techniques, would be MOST effective in parallelizing the data load and improving the overall loading performance?
  • A. Increase the size of the virtual warehouse to the largest available size (e.g., X-Large or higher).
    This will automatically parallelize the data loading process.
  • B. Use Snowpipe with auto-ingest enabled to continuously load data as new files arrive in the S3 bucket.
  • C. Partition the 'SALES FACT table by a relevant date column and then use the 'ON ERROR = CONTINUE option in the COPY INTO statement to skip rows with errors related to partitioning.
  • D. B and C are both correct
  • E. Ensure that the CSV files are split into smaller files (e.g., 10-100MB each). Snowflake automatically parallelizes data loading across multiple files. Configure an auto scaling policy for virtual warehouse.
Answer: E
Explanation:
Snowflake automatically parallelizes data loading across multiple files. Smaller files allow Snowflake to distribute the workload across multiple nodes in the virtual warehouse, thus improving performance. Increasing the warehouse size (Option A) may help to some extent but is not the primary driver of parallelization. Snowpipe (Option C) is a good option for continuous data loading but not directly related to parallelizing a COPY INTO command. Partitioning the table (Option D) is a good practice for query performance but not directly related to improving the loading performance of a COPY INTO command. splitting smaller file and auto scale are independent activities but splitting files into smaller chunks is important.

NEW QUESTION # 19
A Snowflake administrator needs to determine the default collation setting for a newly created database. Which of the following methods can be used to identify the default collation of the database named 'MY DATABASE?
  • A. SHOW DATABASES LIKE 'MY DATABASE';
  • B. SELECT DATABASE COLLATION FROM INFORMATION SCHEMA.DATABASES WHERE
    DATABASE NAME = 'MY DATABASE';
  • C. DESCRIBE DATABASE MY DATABASE;
  • D. SELECT followed by SELECT collation FROM INFORMATION_SCHEMA.DATABASES WHERE database_name =
  • E. SELECT FROM WHERE option_name = 'COLLATION';
Answer: C
Explanation:
'DESCRIBE DATABASE MY_DATABASE;' will show all properties of the database, including the default collation. 'SHOW DATABASES LIKE 'MY DATABASE';' will also show information about the database but does not guarantee displaying the default collation. Options C, D, and E use the INFORMATION_SCHEMA, which is useful, but option A gives direct and complete metadata.
'DATABASE_OPTIONS' table does not exists in information schema. The most efficient method and recommended way is to use `DESCRIBE DATABASE.

NEW QUESTION # 20
......
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