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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with loading a large dataset (50TB) of JSON files into Snowflake. The JSON files are complex, deeply nested, and irregularly structured. You want to maximize loading performance while minimizing storage costs and ensuring data integrity. You have a dedicated Snowflake virtual warehouse (X-Large).
Which combination of approaches would be MOST effective?

A) Pre-process the JSON data using a Python script with Pandas to flatten the structure and convert it into a relational format like CSV. Then, load the CSV files using the COPY INTO command with gzip compression.
B) Load the JSON data using the COPY INTO command with gzip compression. Create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use materialized views to improve query performance.
C) Use Snowpipe with auto-ingest, create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use search optimization on those projected columns.
D) Load the JSON data using the COPY INTO command with no pre-processing. Create a VIEW on top of the raw VARIANT column to flatten the data for querying.
E) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.

2. You have a Snowflake table 'ORDERS with columns 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, and 'TOTAL AMOUNT. You notice that many queries filtering by 'ORDER DATE are slow, even after enabling query acceleration. You decide to implement a caching strategy to improve performance. Which of the following approaches will be most effective in leveraging Snowflake's caching capabilities and improving the performance of date-filtered queries, especially when the data volume for each date is large and varied? Assume virtual warehouse is medium size.

A) Use after running a query filtered by 'ORDER_DATE'. This will cache the result of the query in the current session for subsequent queries with the same filter.
B) Increase the data retention period for the 'ORDERS' table. A longer retention period will ensure that more data is available in the Snowflake cache.
C) Apply a WHERE clause with a date range in all the SELECT statements. This forces the metadata caching.
D) Create a materialized view that pre-aggregates the data by 'ORDER_DATE , such as calculating the sum of 'TOTAL_AMOUNT for each date. This will allow Snowflake to serve the results directly from the materialized view for queries that require aggregation.
E) Create a clustered table on 'ORDER_DATE. This will physically organize the data on disk, allowing Snowflake to quickly retrieve the relevant data for date- filtered queries.

3. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Create separate tables for each "PRODUCT_ID' to improve query performance.
B) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
C) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
D) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
E) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.

4. A data engineer is facing performance issues with a complex analytical query in Snowflake. The query joins several large tables and uses multiple window functions. The query profile indicates that a significant amount of time is spent in the 'Remote Spill' stage. This means the data from one of the query stages is spilling to the remote disk. What are the possible root causes for 'Remote Spill' and what steps can be taken to mitigate this issue? Select two options.

A) The virtual warehouse is not appropriately sized for the volume of data and complexity of the query. Increasing the virtual warehouse size might provide sufficient memory to avoid spilling.
B) The query is using a non-optimal join strategy. Review the query profile and consider using join hints to force a different join order or algorithm.
C) The data being queried is stored in a non-Snowflake database, making it difficult to optimize the join.
D) The 'Remote Spill' indicates network latency issues between compute nodes. There is nothing the data engineer can do to fix this; it is an infrastructure issue.
E) The window functions are operating on large partitions of data, exceeding the available memory on the compute nodes. Try to reduce the partition size by pre- aggregating the data or using filtering before applying the window functions.

5. Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

A) Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
B) Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.
C) Create a replication group in the source AWS account and add the databases and shared objects to it.
D) Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.
E) Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.

Solutions: