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Examinations SOL-C01 Actual Questions & Valid Braindumps SOL-C01 Pdf
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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q169-Q174):NEW QUESTION # 169
Which of the following are benefits of using a multi-cluster warehouse in Snowflake? (Choose any 2 options)
- A. Reduced storage costs
- B. Improved concurrency for handling many simultaneous queries
- C. Automatic scaling to meet varying query loads
- D. Faster data loading
Answer: B,C
Explanation:
Amulti-cluster warehouseprovides Snowflake's highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.
Improved concurrencyis the primary benefit-large numbers of simultaneous queries are distributed across clusters.
Automatic scaling(scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.
Incorrect options:
* Storage costs are unaffected because compute and storage are independent.
* Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.
The design enables smooth operation in high-demand enterprise environments.
NEW QUESTION # 170
A data engineer is tasked with designing a Snowflake solution for a financial services company that needs to perform real-time fraud detection. The solution needs to leverage external models trained in Python using Snowpark ML and ingest data from various sources, including Kafka streams and S3 buckets. Which of the following architectural choices would best leverage the Snowflake A1 Data Cloud capabilities for this scenario?
- A. Create a separate Spark cluster to process Kafka streams and use the Snowflake Spark connector to write the processed data to Snowflake. Use COPY INTO for S3 data, and deploy the ML model using Snowflake external functions calling a Databricks cluster.
- B. Employ Snowflake's Snowpipe for Kafka integration, use Snowflake Tasks to schedule data loading from S3, and leverage Snowflake's native SQL capabilities for fraud detection without employing external models.
- C. Use Snowflake external functions to call a separate SageMaker endpoint for model inference and load data from Kafka using Snowpipe and S3 using COPY INTO.
- D. Utilize Snowflake's Snowpark ML to deploy and manage models directly within Snowflake, integrate with Kafka using Snowflake Kafka connector, and use external tables for S3 data access.
- E. Use Snowflake's Streamlit integration for real-time data visualization of Kafka streams, and use Snowpark ML to deploy and manage models directly within Snowflake, loading data from S3 via COPY INTO.
Answer: D,E
Explanation:
Option B and E offer the best solutions. Option B leverages Snowflake's Snowpark ML for model management within Snowflake, Snowflake Kafka connector for direct Kafka integration, and external tables to avoid data duplication for S3. Option E complements B by adding Streamlit integration for Real Time Visualization of Kafka Streams. Option A, C and D introduce unnecessary external dependencies (SageMaker, Spark, Databricks) and don't fully utilize Snowflake's native capabilities for data processing and ML model deployment. Choosing native capabilities minimizes complexity and operational overhead.
NEW QUESTION # 171
A company stores unstructured text data (PDFs, DOCX) in an external stage (AWS S3). They want to use Snowflake Cortex's PARSE DOCUMENT function to extract specific information, but are encountering performance issues and high costs. Which of the following strategies could optimize performance and reduce costs when using PARSE DOCUMENT in this scenario?
- A. Pre-process the documents to remove irrelevant sections (e.g., boilerplate text, headers, footers) before loading them into Snowflake for parsing. Also, ensure appropriate partitioning of data in the external stage.
- B. Reduce the number of documents being processed in a single batch to minimize memory consumption.
- C. Increase the size of the virtual warehouse used for processing, even if it means paying for larger compute resources, and use 'MAX in the file format configuration.
- D. Implement a robust error handling mechanism to prevent processing from halting due to malformed or corrupted documents and monitor the Snowflake resource consumption using Snowflake's monitoring tools.
- E. Utilize Snowflake's caching mechanism by storing parsed results in a separate table and refreshing it periodically, avoiding redundant parsing of the same documents and reduce MAX FILE SIZE to lower value like 'MAX FILE SIZE-8388608'.
Answer: A,D,E
Explanation:
Option B is correct because pre-processing reduces the amount of data that PARSE_DOCUMENT needs to process. Partitioning in the external stage enables Snowflake to more efficiently retrieve the relevant data. Option C is correct because caching prevents redundant processing and reduce MAX FILE_SIZE to lower value. Option E is correct because error handling ensures processing continues and monitoring provides insights into resource usage. Option A increasing warehouse size and MAX FILE SIZE without other optimizations is often a brute-force approach that doesn't address the root cause of performance problems and leads to unnecessary costs. Option D, limiting batch size, can help with memory issues but doesn't fundamentally improve the efficiency of document parsing.
NEW QUESTION # 172
A developer wants to create a stored procedure in Snowflake using the web UI worksheet. The stored procedure needs to accept a VARIANT data type as input, parse a JSON string within the VARIANT, and insert specific values from the JSON into a table. Which of the following code snippets, when placed inside the stored procedure's body, correctly parses the JSON and inserts the 'name' and 'age' from the VARIANT 'json_data' into the 'employees' table?
Answer: D
Explanation:
Option B is the correct answer. Snowflake uses the colon operator to access elements within a VARIANT data type representing a JSON object. The and are necessary to explicitly cast the extracted JSON values to the appropriate data types for the `employees' table. Options A, C, and D use incorrect syntax for accessing JSON elements within a VARIANT in Snowflake. Option E uses STRING and NIJMBER which may be less precise than VARCHAR and INT, although would function, Option B is best practice.
NEW QUESTION # 173
What is the purpose of the PARSE_JSON function in Snowflake?
- A. Storing JSON data in its PARSE format
- B. Parsing JSON data into relational table format
- C. Converting JSON data to XML format
- D. Loading JSON data from external stages
Answer: B
Explanation:
The PARSE_JSON() function ingests a string containing JSON text and converts it into Snowflake' sVARIANTdata type, enabling the JSON to be queried, navigated, and transformed using SQL. Snowflake does not store JSON in its raw textual representation; instead, VARIANT allows Snowflake to apply optimized parsing, indexing, and querying operations against semi-structured content. This function is particularly useful when JSON arrives inline (e.g., supplied directly within SQL statements or loaded from CSV files containing JSON strings). PARSE_JSON does not perform data loading from stages-that is handled through COPY INTO-nor does it convert JSON into XML. Once JSON is converted to VARIANT, Snowflake allows access to nested structures using dot notation, bracket notation, and functions like FLATTEN(). Thus, the function serves as a bridge between raw JSON strings and Snowflake's relational and analytical capabilities.
NEW QUESTION # 174
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