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

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

1. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
B) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
C) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
D) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
E) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.

2. You're loading data into a Snowflake table using 'COPY INTO'. You notice that some rows are being rejected due to data validation errors (e.g., data type mismatch, uniqueness constraint violations). You want to implement a strategy to capture these rejected rows for further analysis and correction. Which of the following approaches offers the MOST efficient and reliable method for capturing and storing the rejected rows, minimizing performance impact during the data loading process? Assume no staging table exists before loading data to production table.

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

3. You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

A)

B)

C)

D)

E)

4. You are designing a continuous data pipeline to load data from AWS S3 into Snowflake. The data arrives in near real-time, and you need to ensure low latency and minimal impact on your Snowflake warehouse. You plan to use Snowflake Tasks and Streams. Which of the following approaches would provide the most efficient and cost-effective solution for this scenario, considering data freshness and resource utilization?

A) Configure an AWS SQS queue to receive S3 event notifications whenever a new file is uploaded. Use a Lambda function triggered by the SQS queue to invoke a Snowflake stored procedure. This stored procedure executes a COPY INTO command to load the specific file into Snowflake. Use 'ON ERROR = CONTINUE' during COPY INTO.
B) Create a Stream on the target table and a Snowflake Task. The task executes a COPY INTO command into a staging table when the Stream has data and then a MERGE statement. Schedule the task to run continuously with 'WHEN SYSTEM$STREAM HAS but limit the 'WAREHOUSE SIZE' to
C) Create a single, root Snowflake Task that triggers every 5 minutes, executing a COPY INTO command to load all new data from the S3 bucket into a staging table, followed by a MERGE statement to update the target table. Use 'VALIDATE ( STAGE NAME '0'.////' before COPY INTO.
D) Create a Pipe object in Snowflake using Snowpipe and configure the S3 bucket for event notifications to the Snowflake-provided SQS queue. Monitor the Snowpipe status using 'SYSTEM$PIPE STATUS and address any errors by manually retrying failed loads with 'ALTER PIPE REFRESH;'
E) Create a Stream on the target table and a Snowflake Task that runs every minute. The task executes a MERGE statement to apply changes from the Stream to the target table, filtering the Stream data using the 'SYSTEM$STREAM GET TABLE TIMESTAMP function to process only newly arrived data since the last task execution. Use 'WHEN SYSTEM$STREAM HAS to run the Task.

5. You are using the Snowflake REST API to insert data into a table named 'RAW JSON DATA. The JSON data is complex and nested, and you want to efficiently parse and flatten it into a relational structure. You have the following JSON sample:

Which SQL statement, executed after loading the raw JSON using the REST API, is the MOST efficient way to flatten the JSON and extract relevant fields into a new table named 'PURCHASES' with columns like 'EVENT TYPE', 'USER D', 'EMAIL', 'STREET, 'CITY', 'ITEM ID', and 'PRICE'?

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

Solutions: