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

1. 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 D
C) Option E
D) Option A
E) Option C

2. A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

A) Increase the virtual warehouse size.
B) Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
C) Cluster the 'SALES DATA' table on 'CUSTOMER ID.
D) Create a secondary index on 'CUSTOMER ID'
E) Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.

3. You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)

A) Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
B) All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
C) Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
D) Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
E) External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.

4. A data engineer is tasked with migrating data from a large on-premise Hadoop cluster to Snowflake using Spark. The Hadoop cluster contains nested JSON dat a. To optimize performance and minimize data transformation in Spark, what is the most efficient approach to read the JSON data into a Spark DataFrame and write it directly to a Snowflake table?

A) Read the JSON data as strings and utilize Snowflake's 'PARSE JSON' function within a Spark SQL query to transform and load the data into a variant column in Snowflake.
B) Read the JSON data as text files, then use Spark to parse and flatten the JSON structure before writing to Snowflake using the Snowflake JDBC connector.
C) Use the 'STORAGE_INTEGRATION' feature in Snowflake to directly access the JSON files in Hadoop (via an external stage) and load the data without using Spark at all.
D) Use the Snowflake Spark connector with the 'inferSchema' option set to 'true' when reading the JSON data. This allows Spark to automatically infer the schema and write directly to Snowflake.
E) Define a schema manually in Spark, then read the JSON data into a Spark DataFrame. Use the Snowflake Spark connector to write the data to Snowflake, specifying the schema explicitly.

5. You are tasked with implementing a projection policy in Snowflake to restrict access to certain columns of the 'EMPLOYEE table based on the user's role. The table contains columns like 'EMPLOYEE 'NAME, 'SALARY', and 'DEPARTMENT. Users with the 'HR MANAGER role should have access to all columns, while other users should only be able to see 'EMPLOYEE ID, 'NAME, and DEPARTMENT. The initial attempt to create the projection policy results in an error. What could be the reasons?

A) Projection policies are not supported in Snowflake.
B) Projection policies can only be applied at the database level, not at the table level.
C) The user attempting to create the projection policy does not have the 'OWNERSHIP' privilege on the 'EMPLOYEE table.
D) The projection policy definition might contain syntax errors or reference non-existent roles or columns.
E) The EMPLOYEE table must have row-level security policies enabled before applying a projection policy.

Solutions: