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Snowflake DEA-C02 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q310-Q315):

NEW QUESTION # 310
You are tasked with building a data pipeline that ingests data from various sources into Snowflake, processes it, and then writes the final results back to a data lake in AWS S3, partitioned by date. The data in S3 should be queryable by other applications outside of Snowflake. You choose to use Snowflake Iceberg tables for this purpose. Which of the following is the correct SQL statement to create an Iceberg table 'analytics.public.daily_summary' in Snowflake, backed by an S3 bucket 's3://your-bucket/data/daily_summary/', partitioned by the column, and specifying 'parquet' as the file format?

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

Answer: C

Explanation:
The correct syntax for creating an Iceberg table in Snowflake backed by an external location involves using 'USING ICEBERG' and 'EXTERNAL_LOCATION'. 'LOCATION' is used for standard external tables, not Iceberg tables. The 'DATA_SOURCE parameter is not valid in this context. The syntax is specifically designed for creating Iceberg tables and correctly utilizes EXTERNAL_LOCATION to point to the S3 bucket. Note that Iceberg tables requires EXTERNAL_LOCATION rather than LOCATION.

NEW QUESTION # 311
A healthcare provider wants to share patient data with a research organization, but must ensure that researchers only have access to records from a specific region ('REGION A') and only see anonymized data'. You have a 'patients' table with columns 'patient_id' , 'region', 'dob', 'medical history', and 'ssn'. Which of the following steps would be MOST effective and secure for implementing row- level filtering and data masking for this data sharing scenario, minimizing administrative overhead and maximizing query performance?

A. Create a stored procedure that filters the 'patients' table based on the region and applies masking policies. The stored procedure writes the filtered and masked data to a new table. Grant access to the new table to the research organization's role.
B. Create a policy tag on the 'region' column restricting access to 'REGION_A' and apply masking policies directly on the 'patients' table. Grant access to the patientS table to the research organization's role.
C. Create a standard VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A". Create a separate table containing only the anonymized data for 'REGION_A' and grant access to this separate table to the research organization's role.
D. Create a VIEW on the 'patients' table with a WHERE clause filtering by 'region = and use masking policies to redact sensitive fields. Grant access to the view to the research organization's role.
E. Create a secure VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A" and use masking policies to redact sensitive fields. Grant access to the secure view to the research organization's role.

Answer: E

Explanation:
Using a secure VIEW is the most efficient and secure method. It provides row-level filtering (REGION_A) and masking policies, while secure views prevent access to the underlying table's data lineage even with ACCOUNTADMIN privileges. This is preferable to standard views because standard views can expose the underlying table structure. Stored procedures add unnecessary complexity, a separate table duplicates data, and policy tags require governance roles and don't, on their own, handle masking requirements. Also, creating a separate table results in duplicated data and is not efficient and secure and adds administrative overhead.

NEW QUESTION # 312
You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

A. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
B. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.
C. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
D. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
E. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.

Answer: C

Explanation:
Since the Time Travel window (1 day) has passed, the 'UNDROP' command and cloning using or 'BEFORE clauses will not work (eliminating options B and D). While option C might be true if no external backups exist, the most appropriate first step is to contact Snowflake support (option A). This is because, even though Time Travel has expired, Fail-safe might still contain the data, offering a potential recovery path, although it is a longer process and not guaranteed. Option E is also a valid answer.

NEW QUESTION # 313
A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

A. Increase the virtual warehouse size.
B. Create a secondary index on 'CUSTOMER ID'
C. Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
D. Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
E. Cluster the 'SALES DATA' table on 'CUSTOMER ID.

Answer: E

Explanation:
Clustering the table on 'CUSTOMER_ID' will physically organize the data based on this column, improving the performance of queries filtering on 'CUSTOMER ID. While increasing warehouse size (E) might provide some performance boost, clustering addresses the underlying data organization issue. Secondary indexes (A) are not supported in Snowflake. Partitioning by 'CUSTOMER_ID (D) isn't possible in Snowflake. Materialized views (B) are a valid option for pre-aggregation, but clustering will directly improve base table performance for filtering. Therefore, clustering is the best option.

NEW QUESTION # 314
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

A. Increase the parameter for the 'SALES_DATX table.
B. Periodically clone the ' SALES DATA' table to a separate historical table.
C. Decrease the parameter for the 'SALES_DATA' table.
D. Implement data clustering on a column that is frequently used in time travel query filters.
E. Create a materialized view that pre-computes the results of common time travel queries.

Answer: D

Explanation:
Clustering (E) optimizes the physical storage of data, making it more efficient to retrieve specific historical versions. Decreasing data retention (C) can improve performance but reduces the available time travel window. Increasing retention (A) will degrade performance even more. Materialized Views (B) can help, but they require maintenance and may not be suitable for all time travel scenarios. Cloning (D) creates a full copy, which is inefficient and may not be necessary.

NEW QUESTION # 315
......

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