Prepare for the Actual SnowPro Advanced DEA-C02 Exam Practice Materials Collection [Q21-Q40]

NO.21 You are developing a Secure UDF in Snowflake to encrypt sensitive customer data’. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

Granting the EXECUTE privilege on the UDF only to the roles that require access.

Using the ‘SECURE keyword when creating the UDF to prevent viewing the UDF definition.

Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.

Setting the ‘SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.

Using masking policies instead of Secure UDFs is the recommended approach for data security

NO.22 A global e-commerce company, ‘GlobalMart’, uses Snowflake for its data warehousing needs. They operate primarily in the US (us-east-1) and Europe (eu-west-l). They’re implementing cross-region replication for disaster recovery and business continuity. Their requirements are: 1) All data from the US region needs to be replicated to the EU region. 2) The failover to the EU region should have minimal downtime. 3) Replication should be automatic and continuous. Considering these requirements, which of the following Snowflake features and configurations would be the MOST suitable and efficient?

Create a database replica in the EU region and manually refresh it periodically using ‘CREATE DATABASE AS CLONE’

Enable database replication using replication groups, configure a primary database in us-east-I , and a secondary database in eu-west-l. Set the replication schedule with ‘ALTER REPLICATION GROUP ADD .

Use Snowflake’s Data Sharing feature to share data from the US region with an account in the EU region. This automatically replicates the data.

Export data from the US region to cloud storage (e.g., AWS S3 or Azure Blob Storage) and then load it into the EU region using Snowpipe.

Manually unload data from the US region and load it into the EU region using SnowSQL. Automate this process using a scheduled task.

NO.23 You need to implement a data masking solution in Snowflake for a table ‘CUSTOMER DATA’ containing PII. The requirement is to mask the email address based on the user’s role: if the user is in ‘ANALYST ROLE , the email address should be partially masked (e.g., ‘a @example.com’), otherwise, it should be fully masked (e.g., @ .com’). Which of the following masking policy definitions and subsequent actions will correctly implement this?

Create a masking policy ’email_mask’ using ‘REGEXP_REPLACE to replace the first part of the email with asterisks if the current role is not ‘ANALYST_ROLE’ , otherwise use ‘LEFT and ‘ REGEXP_REPLACE to mask only part of the username. Apply this policy to the ‘EMAIL ‘ column of ‘CUSTOMER DATA’.

Create two separate masking policies, one for ‘ANALYST_ROLE’ and one for all other roles. Apply both policies to the ‘EMAIL’ column of ‘CUSTOMER DATA’. Grant the ‘APPLY MASKING POLICY privilege on the ‘CUSTOMER DATA’ table to the ‘ANALYST_ROLE.

Create a masking policy ’email_mask’ using a ‘CASE’ statement that checks ‘CURRENT_ROLE()’. If the role is ‘ANALYST_ROLE, partially mask using ‘LEFT and ‘REGEXP REPLACE; otherwise, fully mask using ‘REGEXP REPLACE. Apply this policy to the ‘EMAIL’ column of ‘CUSTOMER DATA’.

Create a masking policy ’email_mask’ that always fully masks the email address. Grant the ‘UNMASK’ privilege on the ‘EMAIL’ column to the ‘ANALYST ROLE

Create a masking policy ’email_mask’ using a ‘CASE’ statement that checks ‘CURRENT_ROLE()’. If the role is ‘ANALYST_ROLE, partially mask using ‘LEFT and ‘REGEXP REPLACE; otherwise, return original value. Apply this policy to the ‘EMAIL’ column of ‘CUSTOMER DATA’.

NO.24 You have a table ‘CUSTOMERS’ with columns ‘CUSTOMER ID’, ‘FIRST NAME’, ‘LAST NAME, and ‘EMAIL’. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named ‘CUSTOMER DATA’ in a table called ‘CUSTOMER JSON’. The desired JSON structure should include a root element ‘customer’ containing ‘id’, ‘name’, and ‘contact’ fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

Option A

Option B

Option C

Option D

Option E

NO.25 A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called ‘PRODUCTS’ with columns ‘PRODUCT ID’, ‘PRODUCT NAME, ‘CATEGORY, ‘PRICE, and ‘DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

Option A

Option B

Option C

Option D

Option E

NO.26 You are designing a Snowflake alert system for a data pipeline that loads data into a table named ‘ORDERS’. You want to trigger an alert if the number of rows loaded per hour falls below a threshold, indicating a potential issue with the data source. You need to create an alert that is triggered based on the count of rows. Consider the code snippet below and the additional requirements. Assume that the table exists and the connection is successful.

You cannot create alerts based on a rolling hourly window within Snowflake. Alerts can only be based on fixed time intervals.

Create a Snowflake task that runs every hour, executes a query to count the rows loaded in the past hour and triggers an alert using ‘SYSTEM$SEND_EMAIC if the count is below the threshold. No need to create a Snowflake alert.

Create a Snowflake Stream on the ‘ORDERS’ table. Then create an Alert that triggers based on the metadata column, comparing it to the threshold value. This allows for real-time monitoring of data changes.

Create a Snowflake Alert that executes a SQL query to count the number of rows loaded into the ‘ORDERS table within the last hour. Configure the alert to trigger when the count is below the defined threshold. Use a Notification Integration to send alerts to a monitoring system.

Use Snowflake’s Resource Monitor feature and adjust the credit quota to trigger an alert if the credit usage exceeds the threshold for the virtual warehouse processing data pipeline, indirectly indicating that performance is degraded or data volume has changed significantly.

NO.27 You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, ‘task B’ , depends on the successful completion of ‘task A’. ‘task_B’ occasionally fails due to transient network issues. To ensure the pipeline’s robustness, you need to implement a retry mechanism for ‘task_B’ without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for ‘task_B’ is as follows:

Modify the task definition of ‘task_B’ to include a SQL statement that checks for the success of ‘task_R in the TASK_HISTORY view before executing the main logic. If ‘task_A’ failed, use ‘ SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.

Create a separate task, ‘task_C’, that is scheduled to run immediately after ‘task will check the status of ‘task_BS in the TASK HISTORY view. If ‘task_B’ failed, ‘task_c’ will re-enable ‘task_B’ and suspend itself. Use the parameter on ‘task_B’ to limit the number of retries.

Embed the retry logic directly within the stored procedure called by ‘task_B’. The stored procedure should catch exceptions related to network issues, introduce a delay using ‘SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.

Utilize Snowflake’s external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.

Leverage Snowflake’s event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.

NO.28 A Snowflake table ‘ORDERS’ is clustered on the ‘ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of ‘ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

Run ‘OPTIMIZE TABLE ORDERS’ to recluster the table.

Drop and recreate the table with a different clustering key.

Change the ORDER DATE column to VARCHAR and then cluster.

Increase the virtual warehouse size used for loading data into the “ORDERS’ table.

Change the clustering key to a composite key including ORDER DATE and another relevant column.

NO.29 You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use ‘VALIDATE ( STAGE NAME ‘0’.////’ before COPY INTO.

Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use ‘ON ERROR = CONTINUE’ during COPY INTO.

Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with ‘WHEN SYSTEM$STREAM HAS but limit the ‘WAREHOUSE SIZE’ to

Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using ‘SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with ‘ALTER PIPE REFRESH;’

Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the ‘SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use ‘WHEN SYSTEM$STREAM HAS to run the Task.

NO.30 A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named ‘SENSOR READINGS’ which contains columns like ‘DEVICE ID’, ‘TIMESTAMP’, ‘TEMPERATURE’, ‘PRESSURE’, and ‘LOCATION’ (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

Partition the ‘ SENSOR_READINGS table by ‘TIMESTAMP (e.g., daily partitions). Enable search optimization on the ‘LOCATION’ column and enable query acceleration.

Enable Automatic Clustering on ‘DEVICE_ID’ , then enable query acceleration on the virtual warehouse.

Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.

Cluster the table by ‘LOCATION’ and ‘TIMESTAMP , and enable search optimization on the ‘LOCATION’ column, and then enable query acceleration.

Enable search optimization on ‘TEMPERATURE and ‘PRESSURE columns and enable query acceleration.

NO.31 A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table ‘SALES FACT (100 billion rows) and several dimension tables, including ‘CUSTOMER DIM’, ‘PRODUCT DIM’, and ‘DATE DIM’. The queries are run every morning and are essential for business decision-making. The team has identified that the ‘SALES FACT table’s primary key is ‘SALE ID, but the queries frequently filter and join on ‘CUSTOMER and ‘PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.

Enable Automatic Clustering on the ‘SALES FACT table based on ‘CUSTOMER ID’ and ‘PRODUCT ID, then enable query acceleration on the virtual warehouse.

Create materialized views that pre-aggregate the sales data based on ‘CUSTOMER ID’, ‘PRODUCT ID, and ‘DATE ID, then enable query acceleration on the virtual warehouse.

Enable search optimization on the columns ‘CUSTOMER ID’ and ‘PRODUCT ID of the ‘SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.

Enable clustering on the ‘CUSTOMER DIM’ and ‘PRODUCT DIMS tables.

NO.32 You are tasked with optimizing a Snowpipe Streaming pipeline that ingests data from Kafka into a Snowflake table named ‘ORDERS’ You notice that while the Kafka topic has high throughput, the data ingestion into Snowflake is lagging. The pipe definition is as follows: “sql CREATE OR REPLACE PIPE ORDERS_PIPEAS COPY INTO ORDERS FROM @KAFKA STAGE FILE_FORMAT = (TYPE = JSON); Which of the following actions, taken individually, would be MOST effective in improving the ingestion rate, assuming sufficient compute resources are available in your Snowflake virtual warehouse?

Increase the size of the virtual warehouse associated with the Snowflake account.

Implement batching within the Kafka producer to send larger messages.

Tune the parameter in the file format definition to a smaller value.

Enable auto-ingest for the Snowpipe.

Increase the number of Kafka partitions and ensure Snowflake has enough compute to consume them in parallel using Snowpipe Streaming.

NO.33 You are tasked with ingesting data from an external stage into Snowflake. The data is in JSON format and compressed using GZIP. The JSON files contain nested arrays. You need to create a file format object that Snowflake can use to properly parse the dat a. Which of the following options represents the MOST efficient and correct file format definition to achieve this? Assume the stage is already created and accessible.

Option A

Option B

Option C

Option D

Option E

NO.34 You have a table ‘ORDERS in your Snowflake database. You are implementing a new data transformation pipeline. Before deploying the pipeline to production, you want to validate the changes in a development environment. You decide to use Time Travel to create a snapshot of the ‘ORDERS’ table before the transformation and compare it with the transformed data’. Which sequence of SQL commands would best facilitate this validation, assuming your development database and schema structure mirrors production?

NO.35 A data engineering team is managing a Snowflake warehouse that supports a high volume of ad-hoc queries from data analysts exploring a large, semi-structured JSON dataset containing website clickstream data’. The query performance is frequently slow, and analysts are complaining about long wait times. The warehouse is already sized appropriately. You have identified that many of the queries filter on nested JSON attributes that are not explicitly indexed. Considering only query acceleration service features, what is the MOST effective approach to improve query performance for these ad-hoc queries without modifying the queries themselves or significantly increasing storage costs?

Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.

Enable Automatic Clustering on the table to improve data organization based on query patterns.

Enable Search Optimization Service for the table containing the JSON data. Selectively enable search optimization on the specific columns and nested paths that are frequently used in WHERE clause predicates.

Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.

Use a combination of materialized views and query acceleration for best performance.

NO.36 You have a table named ‘ORDERS’ with a column ‘ORDER DETAILS’ that contains JSON data’. You want to extract a specific nested value (‘customer id’) from this JSON data using a SQL UDE The JSON structure varies, and sometimes the ‘customer id’ field might be missing. You need to create a UDF that handles missing fields gracefully and returns NULL if ‘customer id’ is not found. Also, You are looking for a performant solution that is highly scalable. Which of the following SQL UDF definitions is most appropriate?

NO.37 You are building a data pipeline in Snowflake that uses an external function to perform sentiment analysis on customer reviews stored in a table named ‘CUSTOMER REVIEWS’. The external function ‘sentiment_analyzer’ is hosted on AWS Lambda and requires an API key for authentication. You want to ensure that the API key is securely passed to the Lambda function and prevent unauthorized access. Which of the following approaches represents the MOST secure and recommended method to manage the API key?

Store the API key directly in the external function definition as a string literal within the ‘AS’ clause.

Pass the API key as a parameter to the external function each time it is called.

Create a Snowflake secret object to store the API key and reference it in the external function definition using the ‘USING’ clause and ‘SYSTEM$GET SECRET function.

Store the API key in a Snowflake table with restricted access and retrieve it within the external function’s logic.

Embed the API key directly into the AWS Lambda function’s environment variables, avoiding any transmission from Snowflake.

NO.38 You are tasked with optimizing a continuous data pipeline that loads data from an external stage into a Snowflake table using streams.
The pipeline is experiencing significant latency during peak hours. The stream is defined on a very large table with frequent updates and deletes. Which of the following strategies would be MOST effective in reducing the latency of the data pipeline, considering stream performance and cost implications?

Increase the size of the virtual warehouse used for loading data. This will provide more compute resources for processing the stream.

Implement a more aggressive pruning strategy on the base table to reduce the amount of data that the stream needs to track.

Create multiple streams on the same base table, each filtering for specific types of changes (e.g., INSERT, UPDATE, DELETE).

Reduce the RETENTION TIME of the stream. This will limit the amount of historical data tracked and improve performance.

Implement a materialized view on top of the stream to pre-aggregate the data.

NO.39 You have a Snowflake table ‘ORDERS’ with billions of rows storing order information. The table includes columns like ‘ORDER ID’, ‘CUSTOMER ID’, ‘ORDER DATE, ‘PRODUCT_ID’, and ‘ORDER AMOUNT’. Analysts frequently run queries filtering by ‘ORDER DATE’ and ‘CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you’ve already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

Enable search optimization on the ‘ORDER_ID column.

Enable search optimization on the ‘ORDER_DATE’ column.

Enable search optimization on both the ‘ORDER DATE and ‘CUSTOMER IDS columns.

Create a materialized view that pre-aggregates the data based on ‘ORDER_DATE and “CUSTOMER_ID

Enable search optimization on the ‘PRODUCT ID column.

NO.40 You have a Snowflake table, ‘raw_data’, which contains a column ‘data url’ storing URLs pointing to CSV files with varying schemas. Each CSV file represents sales data, but the column names and data types can differ. You need to create a process to automatically discover the schema of each CSV file, load the data into Snowflake, and standardize the column names to ‘order id’, ‘product id’, ‘quantity’, and ‘price’. Which of the following approaches best addresses this requirement, considering scalability and minimal manual intervention?

Create a stored procedure that iterates through each URL in ‘raw_data’ , downloads the CSV file using ‘SYSTEM$URL_GET , parses the CSV header to determine the column names, manually maps the discovered column names to the standardized names, creates a temporary table with the discovered schema, loads the data into the temporary table, transforms the data to use the standardized column names, and then inserts the transformed data into a final target table. Drop the temporary table after successful insertion.

Use Snowpipe with auto-ingest to continuously load the CSV files into a VARIANT column in a staging table. Create a series of views on top of the staging table, each view attempting to extract data based on different potential schema variations. Union all the views together to create a single consolidated view.

Create a Python-based external function that downloads the CSV file from the URL using a library like ‘pandas’, infers the schema using ‘pandas.read_csv’ , maps the discovered column names to the standardized names, and returns the data as a JSON string. Then, create a Snowflake table with a VARIANT column, call the external function for each URL, and load the returned JSON data into the table. Create a view on top of it.

Create a Snowflake external table that points to the external stage. Define a single file format to be used by external table. Define a pipe that uses ‘COPY INTO’ to ingest data into external table from the files found at the file URLs.

Leverage a combination of Snowflake Scripting and External functions: create external function that infer the schema of the CSV, create temporary table based on identified schema, fetch the CSV data using SYSTEM$URL GET using snowflake scripting, copy the data into the temporary table, tranform the data into required structure, ingest into target table and finally drop the temporary table

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