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

1. A Snowflake table 'ORDERS' contains billions of records and is frequently queried for reporting purposes. The reporting queries often filter on 'ORDER DATE and 'CUSTOMER ID'. The data engineering team is considering creating a clustering key to improve query performance. They are evaluating two options: (1) clustering on 'ORDER DATE' alone and (2) clustering on '(ORDER DATE, CUSTOMER ID)'. Which of the following statements best describes the trade-offs between these two options in the context of query performance and data maintenance?

A) Clustering on ORDER DATE alone is preferable because it eliminates the risk of data skewness associated with 'CUSTOMER ID, leading to more balanced micro-partitions and consistent query performance.
B) Clustering on 'ORDER_DATE alone will result in better overall query performance because it's a single dimension, simplifying the clustering process and reducing the need for Snowflake to perform complex data scans.
C) Clustering on '(ORDER DATE, CUSTOMER IDY is always the best option because it allows for more granular filtering and reduces the need to scan unnecessary micro-partitions, regardless of data distribution.
D) Clustering on ' (ORDER DATE, CUSTOMER_ID)' will provide better performance for queries filtering on both columns, but may lead to increased reclustering costs if distribution is skewed within "ORDER_DATE' partitions.
E) Neither clustering option will significantly improve performance, and the team should focus on optimizing the queries themselves through techniques like query rewriting and the use of appropriate indexes.

2. You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A) The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
B) The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
C) The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
D) Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
E) The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.

3. You are tasked with optimizing the performance of a Snowflake virtual warehouse used for running several types of queries: short- running analytical queries with strict latency requirements, long-running batch data transformations, and ad-hoc queries from data scientists. The workload is unpredictable, and the team wants to minimize queueing and maximize resource utilization. Which warehouse configuration would be MOST appropriate to handle this mixed workload, minimizing cost and maximizing performance?

A) A multi-cluster warehouse with a scaling policy of 'Economy' and a minimum of 1 and maximum of 2 clusters with auto-suspend set to 5 minutes.
B) A single X-Large warehouse with auto-suspend set to 5 minutes.
C) Three separate warehouses: a Medium warehouse for analytical queries, a Large warehouse for batch transformations, and an X-Small warehouse for ad-hoc queries.
D) A single Small warehouse with auto-suspend set to 60 minutes.
E) A multi-cluster warehouse with a scaling policy of 'Standard' and a minimum of 1 and maximum of 3 clusters with auto-suspend set to 10 minutes.

4. You are planning to monetize a dataset on the Snowflake Marketplace. You want to provide potential customers with sample data to evaluate before they purchase a full subscription. Which of the following strategies are valid and recommended for offering a free sample of your data within the Snowflake Marketplace? (Select all that apply)

A) Provide the consumer with the script to create a database link to your data, allowing them read-only access to a pre-defined sample table, and then revoke the access after a set period.
B) Create a view that filters the dataset based on a sampling algorithm (e.g., 'SAMPLE ROW' clause) and share the view through the Marketplace.
C) Create a separate share containing a subset (e.g., a smaller number of rows or columns) of the full dataset and offer this share as a free trial listing on the Marketplace.
D) Offer a 'free trial' subscription on the primary listing that automatically expires after a set period (e.g., 7 days), allowing customers to access the full dataset during the trial period. You will need to write custom code to manage trial expiration and data access restrictions based on the trial status.
E) Upload a sample CSV file to a publicly accessible S3 bucket and provide the link in the Marketplace listing description. Consumers can download and load this data into their own Snowflake account for evaluation.

5. A data engineering team is implementing column-level security on a Snowflake table named 'CUSTOMER DATA containing sensitive PII. They want to mask the 'EMAIL' column for users in the 'ANALYST role but allow users in the 'DATA SCIENTIST role to view the unmasked email addresses. The 'ANALYST role already has SELECT privileges on the table. Which of the following steps are necessary to achieve this using a masking policy?

A) Create a masking policy that uses the CURRENT ROLE() function to return a masked value if the current role is 'ANALYST and the original value otherwise.
B) Create a dedicated view on 'CUSTOMER DATA' for analysts with the 'EMAIL' column masked using a CASE statement within the view's SELECT statement. Grant SELECT privilege to the ANALYST role on the view only.
C) Create a masking policy that uses the CURRENT_USER() function to check if the current user belongs to the 'ANALYST' role.
D) Create a masking policy with a CASE statement that checks the CURRENT ROLE() function to see if it's 'ANALYST'. If true, mask the email; otherwise, return the original email.
E) Create a masking policy that uses the IS_ROLE_IN_SESSION('ANALYST') function to return a masked value if the analyst role is active in current session and the original value otherwise.

Solutions: