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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q12-Q17):

NEW QUESTION # 12
You have a data pipeline that loads data from an internal stage into a Snowflake table Craw_data'). The pipeline is experiencing intermittent failures with the error 'SQL compilation error: Stage 'MY INTERNAL STAGE' is immutable'. What are the potential causes of this error and how would you troubleshoot it?

A. Another concurrent process is attempting to drop or alter the internal stage while the COPY INTO command is running. Implement proper locking mechanisms to prevent concurrent modifications.
B. This error is caused by insufficient warehouse size. Increase the warehouse size to accommodate the COPY INTO operation.
C. The user executing the COPY INTO command lacks the necessary privileges (USAGE on the stage). Grant the appropriate privileges to the user or role.
D. The internal stage has been accidentally dropped and recreated with the same name during the COPY operation. Verify the stage's existence and creation timestamp.
E. The internal stage is being used by multiple COPY INTO commands simultaneously, causing a resource contention issue. Implement queuing or throttling mechanisms to manage concurrent data loading.

Answer: A,D

Explanation:
The 'Stage is immutable' error typically indicates that the stage's definition has changed during the COPY operation. This can happen if the stage is dropped and recreated (option A) or if another process is altering the stage concurrently (option C). Privilege issues (option B) would usually result in a different error message. Resource contention (option D) is less likely to cause this specific error but could impact performance. Warehouse size (option E) is generally not directly related to this error.

NEW QUESTION # 13
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.

A. Option B
B. Option C
C. Option A
D. Option D
E. Option E

Answer: A

Explanation:
Option B is the most efficient and correct. The COMPRESSION = 'GZIP' parameter is necessary to handle the GZIP compression. 'STRIP OUTER ARRAY = TRUE is crucial when dealing with JSON files containing nested arrays or a single outer array, which is a common scenario for data coming from external sources. It allows Snowflake to correctly parse each element of the array as a separate row. Option A is incorrect because it doesn't address the potential outer array structure. Option C is incorrect because 'ENABLE OCTAL' is not relevant to the array structure issue. Option D is incorrect as it lacks the compression parameter. Option E is also suitable , but gzip is better performance than AUTO.

NEW QUESTION # 14
You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A. Regularly creating full clones of the 'SALES_DATR table to a separate database.
B. Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
C. Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
D. Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
E. Relying solely on Snowflake's Time Travel feature with the default data retention period.

Answer: B

Explanation:
Option C is correct: Streams provide a granular way to capture DML changes. While Time Travel provides recovery, a stream specifically captures the changes, allowing for more controlled replay and point-in-time recovery. Full clones are resource-intensive, "UNDROP" is for dropped tables, not general recovery, and relying solely on default Time Travel may not meet specific recovery time objectives.

NEW QUESTION # 15
A Snowflake data engineer is troubleshooting a performance issue with a query that retrieves data from a large table (TRANSACTIONS). The table has a VARIANT column containing semi-structured JSON data representing transaction details. The query uses several LATERAL FLATTEN functions to extract specific fields from the JSON and filters the data based on these extracted values. Despite having adequate virtual warehouse resources, the query is running slower than expected. Identify the MOST effective strategy to improve the performance of this query:

A. Create a new table with pre-extracted fields from the VARIANT column and use this table in the query instead of the LATERAL FLATTEN operations.
B. Convert the VARIANT column to a VARCHAR column and store the JSON data as a string.
C. Rewrite the query to use regular expressions instead of LATERAL FLATTEN for extracting the fields from the JSON data.
D. Increase the virtual warehouse size to provide more memory for processing the JSON data.
E. Create a search optimization service on the TRANSACTIONS table for the VARIANT column.

Answer: A

Explanation:
Pre-extracting the required fields from the VARIANT column into separate columns in a new table significantly improves query performance by eliminating the need for expensive LATERAL FLATTEN operations at query time. Option A might help slightly, but pre-extraction is more impactful. Option C is unlikely to be faster. Option D, while applicable to VARIANT columns, is better suited for point lookups, not large-scale extraction and filtering. Option E would negate the benefits of the VARIANT data type.

NEW QUESTION # 16
You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

A. Option B
B. Option A
C. Option C
D. Option D
E. Option E

Answer: B

Explanation:
Option A correctly creates a SECURE VIEW, which is essential for data sharing as it prevents the partner from seeing the underlying table definition. It directly masks the email using SHA256. While Option C uses a hypothetical 'SYSTEM$MASK EMAIL' function, SHA256 is a readily available and standard masking method. Options B, D, and E do not create a secure view or properly grant access for data sharing. The correct method to share is to create secure view and then share using the share object.

NEW QUESTION # 17
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