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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Objectives
Data Transformation and Processing	- Streams and Tasks for ELT pipelines - Handling semi-structured data (JSON, Avro, Parquet) - SQL-based transformations in Snowflake
Performance and Optimization	- Clustering and partition strategies - Warehouse sizing and scaling - Query optimization techniques
Data Engineering Fundamentals	- Snowflake architecture for data engineering - Data pipelines concepts and patterns
Data Ingestion and Integration	- Snowpipe usage and automation - Staging data and loading mechanisms - Batch and streaming ingestion approaches
Security and Data Governance	- Role-based access control (RBAC) - Data masking and encryption - Secure data sharing

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q334-Q339):

NEW QUESTION # 334
A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?

A. Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
B. Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.
C. Drop the current clustered table and create a new table with partition by clauses
D. Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
E. Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.

Answer: B,E

Explanation:
A low 'effective_clustering_ratio' and high 'average_overlaps' indicate that the data is not well-clustered, leading to inefficient query performance. Reclustering the table (A) reorganizes the data based on the clustering key, improving clustering. Creating a schedule (D) ensures that the table remains well-clustered over time. Dropping the clustering key and recreating it with a hash on (B) is unlikely to improve performance for date-range queries. Creating new table via 'CREATE TABLE AS SELECT statement is not the right way. In addition, 'Partition By' clause doesn't exist in Snowflake.

NEW QUESTION # 335
You are troubleshooting a slow-running query that joins a large fact table 'SALES DATA' (100 billion rows) with a smaller dimension table 'CUSTOMER DIM' (1 million rows) on 'CUSTOMER ID. Initial analysis shows that the query is spending significant time in the join operation. You suspect the issue lies with the join strategy being used by Snowflake. Which of the following actions are MOST likely to improve query performance and optimize the join?

A. Convert the query to use a LATERAL FLATTEN function to pre-process the 'CUSTOMER_DIW table before the join.
B. Analyze the query profile in Snowflake's web UI and identify if a broadcast join is occurring. If so, consider increasing session parameter (within limits) or re-designing the query to avoid the broadcast join.
C. Increase the virtual warehouse size and monitor for spillover to local disk. If spilling occurs, further increase the warehouse size.
D. Ensure that the 'CUSTOMER_ID column in both tables has compatible datatypes and that no implicit type conversions are happening during the join. Also check cardinality of 'CUSTOMER_ID in the SALES DATA table.
E. Ensure both 'SALES DATA' and 'CUSTOMER DIM' are clustered on 'CUSTOMER ID.

Answer: B,C,D

Explanation:
Options C, D, and E are the most effective. Increasing the warehouse size (C) provides more resources for the join. Analyzing the query profile for broadcast joins and adjusting 'AUTO BROADCAST JOIN SIZE (D) can prevent inefficient join strategies where the smaller table is unnecessarily broadcast to all nodes. Ensuring data type compatibility (E) prevents performance-impacting implicit conversions. While clustering on the join key (A) can help, it may not be sufficient on its own, especially for very large tables. LATERAL FLATTEN (B) is generally used for semi- structured data and is not relevant to this scenario. Understanding data skewness in 'SALES_DATX is important too. A highly skewed distribution of 'CUSTOMER_ID' could lead to hot spots.

NEW QUESTION # 336
You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

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

Answer: E

Explanation:
Option B correctly addresses all the requirements. It uses 'product_id').cast('string')' to extract the product_id' from the JSON and cast it to a string; filters for non-null 'product_id' values and 'event_time' within the last hour using 'dateadd' and 'current_timestamp' for efficient time filtering; and then groups by 'product_id' to count the events. Option A includes 'to_timestamp' which is unneccessary. Option C extracts the 'product_id' from the JSON string to create 'product_id' without casting it to string type. This could create issues in later aggregations/filtering if the data types do not match up. Option D uses subtraction on 'current_timestamp' with the addition result of 'dateadd('hour', 1, current_timestamp()Y to perform time filtering. This will subtract a future time from the current time. This is incorrect.Option E passes a SQL string directly to the filter, bypassing Snowpark+s function calls.

NEW QUESTION # 337
You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A. Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
B. Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
C. Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
D. Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
E. Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.

Answer: B,D,E

Explanation:
Snowflake encrypts data at rest by default (option A). Using network policies to restrict access based on IP address (option D) provides a layer of network security. Encrypting data client-side (option E) adds another layer of security beyond Snowflake's default encryption. Masking policies (option B) are applied to tables, not directly to the stage. While temporary stages exist, they are related to session lifecycle, not security inherently (option C).

NEW QUESTION # 338
Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A. The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
B. The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
C. The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
D. The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
E. The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.

Answer: C,E

Explanation:
UDTFs in Snowflake require explicit registration using 'session.udtf.register' or 'create or replace function' command, defining the location of the source code (Python file) and the function to be executed. Also, data types of values which is 'yield'ed in the body of UDTF must strictly adhere with Output schema. Libraries from 'snowflake.snowpark' are usually available and does not needs explicit configuration. UDTFs are schema-bound, not automatically available everywhere. Direct call to UDTF without creation isn't possible.

NEW QUESTION # 339
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