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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A) The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.
B) The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.
C) The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.
D) The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.
E) The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.

2. A financial institution needs to mask sensitive customer data (PII) in a 'CUSTOMER' table. The table contains columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME', 'CREDIT CARD, and 'ADDRESS'. The data should be masked differently for different roles: 'ANALYST' role should see obfuscated values for names and addresses, while the 'SUPPORT' role should see the last four digits of the credit card and a hashed version of the address. The "CUSTOMER ID' should never be masked. Assume a central masking policy already exists called 'PII MASKING POLICY. Which of the following statements is the MOST efficient and secure way to achieve this?

A) Create multiple masking policies, one for each role and sensitive column combination, each with the appropriate masking expression. Then, apply each masking policy individually to its respective column. Use the function to implement role-based masking within each policy.
B) Create a single masking policy with a complex stored procedure that checks the current role and applies different masking functions accordingly, then apply this policy to all sensitive columns.
C) Create view for each role which applies masking functions to the columns. Grant SELECT access on those views to relevant roles.
D) Create external functions to handle the complex masking logic and call them from the masking policy.
E) Create multiple masking policies with different masking expressions and apply them directly to the columns based on the role using conditional expressions within the policies. Use 'CASE statements within the masking policy to differentiate between roles.

3. You are tasked with loading Parquet files into Snowflake from an AWS S3 bucket. The Parquet files are compressed using Snappy compression and contain a complex nested schem a. Some of the columns contain timestamps with nanosecond precision. You want to create a Snowflake table that preserves the timestamp precision. Which COPY INTO statement options and table definition are MOST appropriate?

A) Table Definition: CREATE TABLE my_table (ts VARCHAR, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' = PARSE TIMESTAMP(ts));
B) Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' VALIDATION_MODE = RETURN_ERRORS;
C) Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
D) Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE - - pARQUET COMPRESSION = AUTO) ON_ERROR = 'SKIP_FILE';
E) Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';

4. You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A) Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
B) Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.
C) Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
D) Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
E) Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.

5. Consider a table with columns and 'customer _ region'. You want to implement both a Row Access Policy (RAP) and an Aggregation Policy on this table. The RAP should restrict access to orders based on the user's region, defined in a session variable 'CURRENT REGION'. Users should only see orders from their region. The Aggregation Policy should mask order totals for regions other than the user's region when aggregating data'. In other words if someone attempts to aggregate ALL region's totals, the aggregation will only include their region. Which statements about implementing this scenario are true?

A) The Aggregation Policy is evaluated before the RAP, ensuring that even if users try to bypass the RAP by aggregating across all regions, the results will be masked appropriately according to 'CURRENT REGION'.
B) The RAP should be applied first to filter the data, and then the Aggregation Policy will apply to the filtered data, only masking aggregated values within the user's region.
C) You cannot use session variables directly in Row Access Policies; you must pass the session variable as an argument to a user-defined function (UDF) called by the policy.
D) Using external functions in RAPs can introduce performance overhead, especially if the external function is complex or slow to execute.
E) You can use the function within both the RAP and Aggregation Policy to control access based on user roles in addition to region.

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