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

1. You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?

A) Extract the frequently queried key-value pairs from the 'EVENT_DATR JSON into separate virtual columns and enable search optimization on these virtual columns.
B) Increase the warehouse size.
C) Convert the ' EVENT_DATX column to a VARCHAR column and enable search optimization on it.
D) Enable search optimization directly on the 'EVENT DATA' column.
E) Create a materialized view that extracts the key-value pairs from the ' EVENT_DATX column and enable search optimization on the materialized view's columns.

2. A data engineer is tasked with processing a large dataset of customer orders using Snowpark Python. The dataset contains a column stored as a string in 'YYYY-MM-DD HH:MI:SS' format. They need to create a new DataFrame with only the orders placed in the month of January 2023. Which of the following code snippets achieves this most efficiently, considering potential data volume and query performance?

A)

B)

C)

D)

E)

3. You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
B) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
C) Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
D) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
E) Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.

4. You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

A) Option A
B) Option E
C) Option B
D) Option D
E) Option C

5. You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A) The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
B) DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
C) The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
D) The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
E) The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.

Solutions: