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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q152-Q157):

NEW QUESTION # 152
A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A. Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.
B. Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.
C. Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
D. Convert the entire dataset into a single VARIANT column and query using JSON path expressions.
E. Replace the standard JOINs with LATERAL FLATTEN operations.

Answer: A,B

Explanation:
Materialized views can pre-compute the joined data and apply filters, significantly reducing the amount of data the query needs to process at runtime. Clustering keys on the frequently filtered columns in 'ORDERS' and 'CUSTOMERS' will enable data skipping, allowing Snowflake to efficiently prune micro-partitions. Creating indexes is not directly supported in Snowflake, however Clustering is equivalent to Indexing strategy. Increasing warehouse size without analysis is not an appropriate strategy. Lateral Flatten is relevant when dealing with array or semi- structured data, and converting the data into VARIANT would severely impact query performance.

NEW QUESTION # 153
You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)

A. Option D
B. Option C
C. Option A
D. Option B
E. Option E

Answer: C,D,E

Explanation:
Options A, B and E are valid ways to selectively load the data. Option A: specifies the full path to the desired file directly in the FROM clause. Option B: uses the 'FILES' parameter to explicitly list the file to be loaded. Option E: uses PATTERN regular expression to filter the files. Option C is incorrect because the 'WHERE clause is invalid in 'COPY' command. Option D is wrong as it's not a correct directory structure, and also invalid as it is trying to specify folders with year, month, day structure.

NEW QUESTION # 154
You need to create a UDF in Snowflake to perform complex data validation. This UDF must access an external API to retrieve validation rules based on the input data'. You want to ensure that sensitive API keys are not exposed within the UDF's code and that the external API call is made securely. Which of the following approaches is the MOST secure and appropriate for this scenario?

A. Pass the API key as an argument to the UDF when it is called. Rely on the caller to provide the correct key and keep it secure.
B. Store the API key as an environment variable within the UDF's JavaScript code. Snowflake automatically encrypts environment variables for security.
C. Hardcode the API key directly into the UDF's JavaScript code, obfuscating it with base64 encoding.
D. Use a Snowflake Secret to securely store the API key. Retrieve the secret within the UDF using the 'SYSTEM$GET_SECRET function, and use 'SECURITY INVOKER with caution or define the UDF as 'SECURITY DEFINER with appropriate role based access controls .
E. Store the API key in a Snowflake table with strict access controls, and retrieve it within the UDF using a SELECT statement. Use 'SECURITY INVOKER to ensure the UDF uses the caller's privileges when accessing the table.

Answer: D

Explanation:
Using Snowflake Secrets is the most secure method for storing sensitive information like API keys. 'SYSTEM$GET_SECRET allows you to retrieve the secret within the UDF without exposing the key in the code. Using 'SECURITY INVOKER should be done with caution to not broaden access unintentionally. Storing API keys in tables, passing them as arguments, environment variables, or hardcoding them are all insecure practices.

NEW QUESTION # 155
A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A. Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
B. Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
C. Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
D. Use the 'UNDROP DATABASE PRODUCTION command.
E. Contact Snowflake Support and request restoration from Fail-safe.

Answer: A

Explanation:
Fail-safe is a last resort for data recovery managed entirely by Snowflake. Users cannot directly access or restore data from Fail- safe. Options B and C are valid for Time Travel, but fail if the data falls outside of that window. Option A is partially correct; you contact Snowflake support, who then might use fail-safe if appropriate, but option E is the most accurate answer. Option D uses Time Travel, which may not work.

NEW QUESTION # 156
You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
B. Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
C. Set 'snowflake.ingest.file.name' to an existing file in a stage.
D. Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
E. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.

Answer: E

Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.

NEW QUESTION # 157
......

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