DEA-C02 - Perfect Visual SnowPro Advanced: Data Engineer (DEA-C02) Cert Test

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q235-Q240):

NEW QUESTION # 235
Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like 'product id', 'name', and 'price'. However, sometimes the 'price' field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns 'product_id', 'name', and 'price' (defined as NUMBER). How can you handle the missing 'price' field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?

A. Define the 'price' column in the Snowflake table as VARIANT. After the data is loaded, create a view that extracts the price using the 'GET' function and converts it to NUMBER using 'TO NUMBER. Handle NULL values in the view using 'price')), NULL, TO 'price')))'.
B. Define the 'price' column in the Snowflake table as NUMBER and use a transformation within the COPY INTO statement to handle missing prices: 'TRANSFORMATION = (price =
C. Pre-process the JSON data before loading into Snowflake, adding a 'price': null field to any product object missing the price.
D. Define the 'price' column in the Snowflake table as VARCHAR. During data loading use the NULLIFEMPTY function within the COPY INTO statement: 'TRANSFORMATION=
E. Define the 'price' column in the Snowflake table as NUMBER and use the DEFAULT NULL clause. The COPY INTO statement will automatically insert NULL values for missing fields.

Answer: B

Explanation:
Option B is the most effective approach using Snowflake's built-in features. TRY TO NUMBER will return NULL if 'price' is missing, ensuring that NULL is inserted into the NUMBER column, avoiding errors. Option A does not work, DEFAULT NULL is not respected on column mappings. C does not work since VARCHAR and NULLIFEMPTY do not meet all the requirments. Option D works, but is not the most efficient. Option E requires unnecessary pre-processing.

NEW QUESTION # 236
You are ingesting data from an AWS S3 bucket into a Snowflake table using a COPY INTO statement. The COPY INTO command fails with an error indicating 'Invalid stage location specified'. You have verified that the stage name is correct and the Snowflake user has the necessary privileges to access the stage. However, the error persists. Which of the following are potential causes and solutions for this issue?

A. The IAM role associated with the Snowflake stage is incorrect or does not have sufficient permissions to access the S3 bucket. Verify the IAM role configuration and permissions.
B. The network policy configured in Snowflake is blocking access to the AWS S3 endpoint. Check the network policy rules and ensure they allow outbound traffic to the S3 region.
C. The external stage definition in Snowflake includes an incorrect storage integration. Examine and correct the STORAGE INTEGRATION parameter in the CREATE STAGE statement.
D. The S3 bucket is encrypted using KMS and the Snowflake integration lacks the necessary key grant. Check the KMS key policy to ensure the storage integration IAM role has decrypt permission.
E. The S3 bucket policy is not correctly configured to allow Snowflake to assume the IAM role. Review the bucket policy to ensure it grants access to the Snowflake IAM role.

Answer: A,C,D,E

Explanation:
The 'Invalid stage location specified' error can be misleading. It often masks underlying issues with permissions, network connectivity, or encryption. Options A, B, C, and E represent the most common causes. IAM role misconfiguration (A), incorrect S3 bucket policy (B), invalid STORAGE INTEGRATION (C), and missing KMS key grants (E) can all lead to this error. Network Policy issues though possible are less likely if other S3 access is working and the error message is typically more direct.

NEW QUESTION # 237
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
B. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
C. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
D. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
E. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.

Answer: C

Explanation:
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention

NEW QUESTION # 238
You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

A. Use ' STRTOK TO ARRAY' to split the string into an array, then iterate through the array using a JavaScript UDF to create the JSON objects.
B. Use to split the string into rows, then use 'SPLIT to separate item name and quantity, and finally use 'OBJECT_CONSTRUCT and to create the JSON array.
C. Utilize a Java UDF to parse the string and directly generate the JSON array.
D. Use 'SPLIT with ';' as delimiter, then apply 'SPLIT again with ':' as delimiter. Finally, construct the JSON array using 'ARRAY_AGG' and 'OBJECT CONSTRUCT
E. Use ' to extract item names and quantities, then use 'ARRAY_CONSTRUCT and 'OBJECT_CONSTRUCT to create the JSON array.

Answer: B,D

Explanation:
Options A and D correctly outline the process. (A) and multiple 'SPLIT calls (D) are valid approaches to break down the concatenated string. Then, 'OBJECT_CONSTRUCT builds the individual JSON objects, and aggregates them into a JSON array. While Javascript or Java UDFs (C, E) could solve the problem, they are generally less efficient than Snowflake's built-in functions. (B) might work but is overkill for this simple splitting task, also you would still need to combine the extracted arrays for items and quantities.

NEW QUESTION # 239
You are designing a data governance strategy for a Snowflake data warehouse. One of the key requirements is to track data lineage for sensitive data, specifically Personally Identifiable Information (PII). You need to understand how PII data flows through various transformations and tables. Which Snowflake feature, when combined with appropriate tagging and metadata management practices, can BEST help you achieve this?

A. Snowflake Secure Data Sharing
B. Snowflake's Resource Monitors
C. Snowflake INFORMATION_SCHEMA views and Account Usage views
D. Snowflake Cloning
E. Snowflake Data Masking policies

Answer: C

Explanation:
Snowflake's INFORMATION_SCHEMA views (e.g., COLUMNS, TABLES, VIEWS) and Account usage views (e.g., QUERY_HISTORY, ACCESS_HISTORY) provide metadata about database objects and query execution history. By tagging PII columns and tracking queries that access these columns, you can trace data lineage. Data Masking policies protect data at rest and in transit. Secure Data Sharing allows sharing of data without physical copying. Cloning creates a copy of data. Resource monitors help manage costs.

NEW QUESTION # 240
......

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