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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Data Architecture and Processing	20%	- Data Pipeline Design 1. Data scheduling and orchestration 2. Pipeline monitoring and error handling 3. Stream and task patterns - Data Modeling for Performance 1. Slowly changing dimensions (SCD) 2. Dimension handling 3. Star and snowflake schemas - Data Storage Architecture 1. Micro-partitioning and clustering 2. Table types (Permanent, Transient, Temporary) 3. Hybrid Tables concepts
Security and Governance	15%	- Data Security 1. Data masking and tokenization 2. Row-level security policies 3. External tokenization 4. Column-level security - Access Control 1. Role hierarchy and ownership 2. GRANT and REVOKE operations 3. Role-based access control (RBAC) - Governance and Compliance 1. Object tagging 2. Data retention policies 3. Access history and auditing 4. Row access policies
Data Ingestion and Consumption	20%	- Bulk Loading and Unloading 1. File format options (CSV, JSON, Parquet, AVRO) 2. Handling staged files 3. COPY INTO command options and best practices 4. Data loading performance optimization - Continuous Data Loading 1. Real-time data ingestion patterns 2. Automating data loading with tasks 3. Snowpipe configuration and usage - Data Unloading 1. Partitioning unloading data 2. Unloading to internal and external stages 3. Data export best practices
Performance Optimization	15%	- Query Optimization 1. Indexing strategies with clustering 2. Query result caching 3. Query profiling and analysis 4. Avoiding common performance pitfalls - Data Optimization 1. Search optimization service 2. Materialized views 3. Data cache management - Warehouse Performance 1. Resource monitors 2. Multi-cluster warehouses 3. Warehouse sizing and selection 4. Warehouse scaling policies
Data Transformation with Snowflake	30%	- SQL Transformations 1. Window functions advanced usage 2. Complex JOINs and set operations 3. Working with semi-structured data (VARIANT) 4. Data type conversions and handling - Snowflake Scripting 1. Dynamic SQL 2. Error handling 3. Procedures and control flow - Data Processing Patterns 1. Time travel and change data capture 2. Zero-copy cloning for ETL 3. MERGE, UPDATE, DELETE operations

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q155-Q160):

NEW QUESTION # 155
A Snowflake table 'CUSTOMER ORDERS is clustered by 'ORDER DATE. You have observed the clustering depth increasing over time, impacting query performance. To improve performance, you decide to recluster the table. However, you need to minimize the impact on concurrent DML operations and cost. Which of the following strategies would be MOST effective in managing this reclustering process?

A. Use 'CREATE OR REPLACE TABLE with SELECT FROM CUSTOMER ORDERS to rebuild the table with optimized clustering.
B. Implement a continuous reclustering process using Snowpipe to automatically recluster new data as it arrives.
C. Recluster the entire table in a single transaction during off-peak hours.
D. Leverage Snowflake's automatic reclustering feature, monitor its performance, and adjust warehouse size as needed.
E. Create a new table clustered by 'ORDER_DATE, copy data in parallel, and then swap tables.

Answer: D

Explanation:
Snowflake's automatic reclustering feature is designed specifically to address this scenario. It automatically reclusters data in the background, minimizing the impact on concurrent DML operations. Options A and E will lock the table and can cause performance impacts during peak hours. Option B is more complex and requires significant downtime during the swap. Option C describes Snowpipe data ingestion, which isn't reclustering. While creating clustered tables with Snowpipe is possible, this option does not address the already unclustered data. Adjusting the warehouse size is relevant to reclustering performance but is secondary to enabling the automatic reclustering itself.

NEW QUESTION # 156
You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?

A. The maximum retention period is 90 days for Enterprise Edition or higher. You can set it using: 'ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 90;'
B. The maximum retention period depends on your Snowflake edition and can be set at the account level only.
C. The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
D. The maximum retention period is 90 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
E. The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'

Answer: A

Explanation:
For Snowflake Enterprise Edition (or higher), the maximum Time Travel retention period is 90 days. The 'ALTER TABLE ... SET DATA RETENTION_TIME IN DAYS' command allows setting the retention period at the table level. Option D is partially correct about editions impacting limits, but incorrect about account-level settings only.

NEW QUESTION # 157
A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A. Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
B. Create separate tables for each "PRODUCT_ID' to improve query performance.
C. Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
D. Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
E. Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.

Answer: C

Explanation:
Analyzing the cardinality and data distribution (Option B) is crucial to understanding the effectiveness of search optimization. If 'PRODUCT_ID has skewed data distribution, search optimization might not be as effective. helps estimate the number of unique values, and histograms reveal the distribution. While estimating the cost of search optimization (Option A) is good practice, it doesn't directly address the potential skewness issue. Clustering (Option C) is a different optimization technique, and dropping/recreating the table (Option D) is a drastic measure without evidence of corruption. Creating separate tables for each 'PRODUCT_ID is not scalable and will drastically increase maintenance overhead.

NEW QUESTION # 158
You are designing a Snowflake data pipeline that continuously ingests clickstream dat a. You need to monitor the pipeline for latency and throughput, and trigger notifications if these metrics fall outside acceptable ranges. Which of the following combinations of Snowflake features and techniques would be MOST effective for achieving this goal?

A. Create a custom dashboard using a Bl tool that connects to Snowflake via JDBC/ODBC and visualizes data ingestion and processing metrics. Manually monitor the dashboard for anomalies.
B. Rely on Snowflake's default resource monitors to track warehouse usage. If warehouse usage exceeds a certain threshold, assume there are performance issues and send a notification.
C. Implement a combination of Snowflake Streams, Tasks, and external functions. Streams capture changes, Tasks process the changes, and external functions send notifications to a monitoring service when latency or throughput issues are detected.
D. Use Snowflake's Event Tables and Event Notifications to capture events related to data ingestion and processing. Configure alerts based on event patterns that indicate latency or throughput issues.
E. Use Snowflake's 'QUERY_HISTORY view to track query execution times and implement a scheduled task that queries this view, calculates latency and throughput, and sends email notifications using Snowflake's built-in email integration if thresholds are exceeded.

Answer: C,D

Explanation:
Options B and D offer the most effective solutions. Option B provides a granular approach using Streams, Tasks, and external functions for real-time monitoring and notification. Option D leverages Event Tables and Event Notifications, enabling a reactive approach based on specific event patterns. Option A is less precise as it relies on query history, which may not accurately reflect real-time latency. Option C is too general. Option E requires manual monitoring, which is not ideal for continuous pipelines.

NEW QUESTION # 159
You are tasked with processing streaming data in Snowflake using Snowpark Python. The raw data arrives in a DataFrame raw events' with the following schema: 'event id: string', 'event_time: timestamp', 'user id: string', and 'event data: string'. You need to perform the following data transformations: 1 . Extract a specific value from the JSON 'event_data' using the 'get' function to find the 'product_id' and create a new column named 'product id' of type STRING. 2. Filter the DataFrame to include only events where the is NOT NULL and the is within the last hour. 3. Aggregate the filtered data to count the number of events per 'product id'. Which of the following code snippets correctly performs these transformations in an efficient and performant manner?

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

Answer: B

Explanation:
Option B correctly addresses all the requirements. It uses 'product_id').cast('string')' to extract the product_id' from the JSON and cast it to a string; filters for non-null 'product_id' values and 'event_time' within the last hour using 'dateadd' and 'current_timestamp' for efficient time filtering; and then groups by 'product_id' to count the events. Option A includes 'to_timestamp' which is unneccessary. Option C extracts the 'product_id' from the JSON string to create 'product_id' without casting it to string type. This could create issues in later aggregations/filtering if the data types do not match up. Option D uses subtraction on 'current_timestamp' with the addition result of 'dateadd('hour', 1, current_timestamp()Y to perform time filtering. This will subtract a future time from the current time. This is incorrect.Option E passes a SQL string directly to the filter, bypassing Snowpark+s function calls.

NEW QUESTION # 160
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