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

1. You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A) Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
B) Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
C) Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.
D) Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
E) Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.

2. A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

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

3. You are tasked with building a User-Defined Aggregate Function (UDAF) in Snowflake to calculate the weighted average of product prices. The weight is determined by the quantity sold for each product. You have the following table: 'SALES (product_id INTEGER, price quantity INTEGER)'. Which of the following UDAF definitions would correctly calculate the weighted average?

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

4. 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 C
C) Option A
D) Option E
E) Option D

5. You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A) Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
B) Increase the number of Snowflake virtual warehouses to handle the increased load.
C) Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
D) Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
E) Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.

Solutions: