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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q243-Q248):

NEW QUESTION # 243
You are tasked with ingesting data from a REST API that provides customer order information in JSON format. The API returns a nested JSON structure with an array of orders, each containing customer details and order items. The data volume is expected to be high. You need to efficiently load this data into Snowflake. Which of the following approaches would be MOST efficient, considering cost and performance, and taking into account potential data quality issues?

A. Use an external function to call a Python script that parses the JSON, transforms the data into a flat relational structure, and then uses the Snowflake Python Connector to insert the data directly into relational tables.
B. Load the JSON data directly into a VARIANT column, then create a view to flatten the data as needed. Use Snowflake's JSON functions for querying and transformation.
C. Use Snowpipe with a stream and task to continuously ingest the JSON data into a staging table with a VARIANT column. Then, create a separate task to flatten the data into relational tables using SQL transformations after ingestion.
D. Use a third-party ETL tool to extract, transform, and load the data into Snowflake.
E. Load the data into a temporary stage, manually parse it with SQL statements, and then load it into your target tables. Schedule the task for periodic execution.

Answer: C

Explanation:
Snowpipe with a stream and task offers continuous, near real-time ingestion. Using a staging table with a VARIANT column allows for handling the complex JSON structure initially. Subsequent tasks can then transform and flatten the data into relational tables. This approach combines efficiency, automation, and scalability, minimizing manual intervention and maximizing performance for high-volume data. Other options are less efficient or scalable: A requires constant parsing during queries, C introduces external dependencies and overhead, D adds complexity and cost of a third-party tool, and E is manual and not scalable.

NEW QUESTION # 244
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

A. Use a subquery to find the top 10 'product_id' based on total revenue, then join this subquery with 'sales_data' and use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)' to calculate the cumulative revenue.
B. Use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey to calculate the cumulative revenue for all products, then filter the results to include only the top 10 products based on their final cumulative revenue.
C. Use a window function to rank products by total revenue, then filter for the top 10 ranks using a 'QUALIFY' clause. Finally, calculate cumulative revenue using 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey.
D. Use the aggregation to identify top 10 'product_id' and calculate the cumilative revenue using SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)'.
E. Create a temporary table to store the total revenue for each product, then select the top 10 from the temporary table. Join this result with 'sales_data' and apply ' SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey for the cumulative revenue calculation.

Answer: C

Explanation:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.

NEW QUESTION # 245
You are tasked with creating a data access strategy for a marketing analytics team. They need access to customer purchase data, but only aggregated by region and product category. They should not be able to see individual customer details due to PII compliance. You decide to use a Secure View. Which of the following are the MOST appropriate steps to ensure data security and minimize performance impact?

A. Create a Materialized View that aggregates the data. Create a Secure View on top of the Materialized View and grant SELECT privilege on the secure view to the marketing analytics role.
B. Create a Secure View that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
C. Create a Secure View directly on the base tables with the aggregation logic. Grant SELECT privilege on the view to the marketing analytics role.
D. Create a regular view that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
E. Create a Materialized View directly on the base tables with the aggregation logic. Grant SELECT privilege on the Materialized View to the marketing analytics role.

Answer: B

Explanation:
Secure Views prevent access to the underlying tables and data lineage. Creating the Secure View directly with the aggregation prevents unauthorized access to detailed data and avoids exposing intermediate tables or views. Materialized views while improving performance, when used along with a Secure View adds unnecessary complexity as aggregation should happen in secure view only.

NEW QUESTION # 246
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

A. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.
B. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.
C. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
D. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
E. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.

Answer: A

Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

NEW QUESTION # 247
You've created a Snowflake dashboard for your company's sales team using a Bl tool. The dashboard displays real-time sales data pulled directly from Snowflake. The sales team wants to be automatically notified whenever a particular product's sales exceed a certain threshold in a given day. You need to implement a system to accomplish this. Which of the following methods offer a way to trigger notifications based on data changes in Snowflake AND integrate with an external notification system (e.g., Slack, Email)? Select all that apply:

A. Snowflake Replication: Replicate the sales table to a separate Snowflake account. Monitor the replicated table for threshold breaches, and use Snowflake Scripting to send email.
B. BI Tool Scheduling: Configure the BI tool to run a scheduled query that checks for the sales threshold. If the threshold is exceeded, the BI tool can send an email or trigger a webhook to send a notification.
C. Snowflake Streams and Pipes: Create a stream on the sales table and a pipe that loads data into a separate notification table. Configure an external service to monitor the notification table and send alerts when new records are inserted.
D. Snowflake Alerts: Create an alert that triggers when a specific query condition (sales exceeding the threshold) is met. Configure the alert to call an external function (e.g., AWS Lambda) which then sends a notification to the desired system.
E. Snowflake Tasks: Schedule a daily task to run a query checking for sales exceeding the threshold. If the condition is met, the task can execute a stored procedure that calls an external API to send a notification.

Answer: D,E

Explanation:
A and B offer native Snowflake capabilities for triggering actions based on data conditions and integrating with external systems. Alerts are designed for real-time monitoring and can call external functions to send notifications. Tasks can be scheduled to perform periodic checks and trigger notifications through stored procedures and external APIs. C: Streams and Pipes are primarily for continuous data loading and transformation, not direct notification triggering based on data conditions. It's an indirect method. D: BI tools are not the primary component for handling real time alerting based on exceeding specific values. E: Replication is for disaster recovery/high availability and doesn't address real- time notifications.

NEW QUESTION # 248
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