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

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Prüfungsfragen mit Lösungen:

1. You are tasked with creating a Snowpark Python UDF that calculates the exponential moving average (EMA) of a time series dataset stored in a Snowflake table named 'SALES DATA'. The table has columns 'TIMESTAMP' (TIMESTAMP_NTZ) and 'SALES' (NUMBER). The EMA should be calculated for each product, identified by the 'PRODUCT ID' column. You want to optimize the calculation by using a Pandas DataFrame within the UDF and leveraging vectorized operations. Which of the following code snippets would be the MOST efficient and correct way to achieve this? Assume 'alpha' is a predefined float variable representing the smoothing factor.

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

2. A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

A) Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.
B) Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
C) Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.
D) Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
E) Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.

3. A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A) Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.
B) Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.
C) Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
D) Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
E) Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.

4. You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

A) The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.
B) The WITH clause is unneccessary.
C) There is no code provided, so there is nothing wrong with it.
D) Without masking poliy code, it's impossible to determine if there is anything wrong.
E) The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.

5. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
B) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
C) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
D) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
E) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.

Fragen und Antworten: