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Snowflake DEA-C02 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q178-Q183):

NEW QUESTION # 178
A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested daily into Snowflake. The ingestion process currently uses a single 'X-Large' virtual warehouse. During peak hours, the data loading latency increases significantly, impacting downstream reporting. The team is considering either scaling up to a '3X-Large' warehouse or scaling out by creating a multi- cluster warehouse with a minimum of 2 and a maximum of 4 'X-Large' clusters. Which of the following factors should be prioritized when making this decision to optimize performance, considering cost and concurrency requirements?

A. The impact on other workloads sharing the same virtual warehouse, favoring scaling up to isolate the data loading process from other query activities.
B. The budget constraints and the higher per-second cost of a larger warehouse compared to the potential cost savings from reduced overall execution time.
C. The anticipated number of concurrent data loading jobs and the ability of Snowflake to automatically distribute these jobs across multiple clusters.
D. The complexity of the JSON data and the potential for improved parsing performance by a larger, single warehouse due to increased memory.
E. The type of JSON processing used. If using SQL functions like PARSE JSON, scaling out is more beneficial than scaling up.

Answer: B,C,E

Explanation:
Scaling out (multi-cluster warehouse) is generally better for concurrency because Snowflake can distribute data loading jobs across multiple clusters. Scaling up provides more resources for a single job, potentially improving its performance, but doesn't address concurrency as effectively. Cost is crucial because larger warehouses are more expensive per second. Finally, the PARSE_JSON function's ability to be parallelized across multiple clusters provides performance benefits.

NEW QUESTION # 179
You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A. The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.
B. The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.
C. There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.
D. The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.
E. The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.

Answer: A,D,E

Explanation:
The problem states that the snowpipe is active, but 'pendingFileCount' remains zero. This means that the events are not making its way to snowpipe. Insufficient permissions (A) on the IAM role will prevent Snowflake from accessing the files. Incorrect event notification settings (B) would stop events from being sent to the queue/topic. Mismatched file format will not generate any events, and will cause files to be skipped. Incorrect message retension period may result in loss of messages before snowpipe could process them, hence can be the root cause. (C) is incorrect as it does not impact picking up of event notifications. Warehouse size mainly affects processing data and not the notification retrieval.

NEW QUESTION # 180
A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?

A. The Account Usage view 'POLICY REFERENCES coupled with 'QUERY HISTORY, filtering for 'ALTER TABLE MODIFY COLUMN SET MASKING POLICY statements and also comparing snapshots of the 'POLICY_REFERENCES' view over time.
B. The 'INFORMATION SCHEMA.POLICY REFERENCES view to determine what masking policies are currently in place. Then, combine that with the use of Snowflake's Alerting framework to get notified on the creation/removal of tables, and also on changes on the masking policies via SYSTEM$GET_PRIVILEGES() function.
C. Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
D. Snowflake's native Data Lineage feature automatically captures all changes to data masking policies without any additional configuration, and those changes are then available to the data steward through the user interface.
E. The 'OBJECT DEPENDENCIES' view in the ACCOUNT USAGE schema will directly track changes related to masking policies applied to tables since that is the best place for lineage information.

Answer: A

Explanation:
The most effective method to audit changes is to combine , showing the current policy assignments, with the 'QUERY HISTORY to identify when those assignments were changed. Specifically, looking for 'ALTER TABLE ... MODIFY COLUMN ... SET MASKING POLICY statements in the 'QUERY_HISTORY will pinpoint the exact changes. Periodically comparing snapshots of 'POLICY_REFERENCES allows you to see which policies were added or removed. Account Usage views track object dependencies, but don't directly show historical changes to policy assignments. Data Lineage focuses on data flow, not policy change auditing. Though has value, it is more appropriate to combine with'QUERY_HlSTORY' since it displays the current masking policies, but will not capture the audit information required without 'QUERY HISTORY and snapshots.

NEW QUESTION # 181
A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?

A. Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.
B. Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
C. Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
D. Drop the current clustered table and create a new table with partition by clauses
E. Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.

Answer: A,E

Explanation:
A low 'effective_clustering_ratio' and high 'average_overlaps' indicate that the data is not well-clustered, leading to inefficient query performance. Reclustering the table (A) reorganizes the data based on the clustering key, improving clustering. Creating a schedule (D) ensures that the table remains well-clustered over time. Dropping the clustering key and recreating it with a hash on (B) is unlikely to improve performance for date-range queries. Creating new table via 'CREATE TABLE AS SELECT statement is not the right way. In addition, 'Partition By' clause doesn't exist in Snowflake.

NEW QUESTION # 182
You have a Snowflake table named 'ORDERS clustered on 'ORDER DATE. After a significant data load, you want to evaluate the effectiveness of the clustering. Which of the following SQL queries, using Snowflake system functions, will provide insights into the clustering depth and overlap of micro-partitions in the 'ORDERS' table, specifically helping you identify whether re-clustering is necessary? Assume that the table

Answer: B

Explanation:
The query SELECT avg_depth, avg_overlap FROM is the correct approach. The function, when given the table name and the clustering key column(s), returns information about the clustering state. Using 'TABLE()' allows you to extract 'avg_deptm and 'avg_overlap', which are key metrics for assessing clustering effectiveness. 'avg_depth' indicates how well the data is clustered (lower is better), and 'avg_overlap' indicates the degree of overlap between micro-partitions (lower is better). A high 'avg_depth' or 'avg_overlap' suggests the need for re-clustering. Option A returns a JSON which is difficult to process to get the required metrics. Option B is missing the clustering key. Option C returns JSON and not the desired output. Option E is not valid SQL syntax in Snowflake.

NEW QUESTION # 183
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