Snowflake DEA-C02

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

1. You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A) The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.
B) The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
C) The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.
D) The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.
E) There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.

2. You are the provider of a data product on the Snowflake Marketplace. You need to grant a trial access to a potential consumer You want to provide limited access for 7 days to specific tables in your database. Which of the following steps are REQUIRED to accomplish this?
(Select all that apply)

A) Grant OWNERSHIP on the specific tables to the consumer's account temporarily.
B) Monitor the consumer's query history to ensure they are only accessing the allowed tables.
C) Contact Snowflake support to enable trial access for the consumer's account.
D) Create a new share specifically for the trial consumer, granting USAGE privilege on the database and SELECT privilege on the specific tables.
E) Create a new role, grant USAGE privilege on the database and SELECT privilege on the specific tables to this role, and then grant this role to the trial consumer.

3. A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A) Use 'session.read.parquet' to load the model file directly into a Snowpark DataFrame and then use 'DataFrame.foreach' to process each row.
B) Load the model from the stage into a DataFrame, then use 'df.mapPartitionS to apply the model to each partition.
C) Create a UDF with gudf(packages=['snowflake-snowpark-python', 'scikit-learn'], input_types=[ArrayType(StringType())], return_type=FloatType(), replace=True, is_permanent=True, and load the model within the UDF's initialization using 'session.file.get' .
D) Use '@vectorized' decorator from Snowpark to process each batch of data passed to the UDF and load the model inside it. Specify the appropriate data types in the decorator.
E) Create a UDF that reads the model from the stage for each row that is passed to it using 'session.file.get' inside the UDF's execution logic.

4. A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A) Drop the clustering key altogether to avoid re-clustering costs.
B) Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).
C) Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.
D) Increase the warehouse size to accelerate the re-clustering process.
E) Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.

5. 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 TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
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 VARCHAR, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' = PARSE TIMESTAMP(ts));
D) 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';
E) 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';

Solutions: