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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion	15-20%	- Continuous Data Ingestion 1. Serverless compute for Snowpipe 2. Real-time data ingestion patterns 3. Snowpipe configuration and usage - Bulk Data Loading 1. File format considerations (CSV, JSON, Parquet, Avro) 2. Staging configurations (internal and external stages) 3. COPY INTO command options and best practices 4. Data loading error handling and validation
Data Governance and Security	15-20%	- Compliance and Monitoring 1. Access history and audit trails 2. Data classification 3. Tag-based policies - Data Protection 1. Data re-identification risks 2. Data masking and tokenization 3. Secure data sharing across accounts 4. External tokenization - Access Control 1. Role-based access control (RBAC) 2. Row-level and column-level security 3. Role hierarchy and ownership 4. GRANT and REVOKE operations
Performance Engineering	10-15%	- Query Optimization 1. Predicate pushdown optimization 2. Query rewrite and optimization techniques 3. Join elimination and simplification - Warehouse Management 1. Resource monitors and cost control 2. Multi-cluster warehouse configuration 3. Auto-suspend and auto-resume settings
Data Transformation	20-25%	- Streams and Tasks 1. Scheduled and event-based task execution 2. Serverless task warehouse configuration 3. Task graph design and dependencies 4. Change Data Capture (CDC) with streams - Data Processing Patterns 1. Zero Copy Cloning for data provisioning 2. ELT vs ETL approaches in Snowflake 3. Time Travel for historical data access - SQL-Based Transformations 1. Window functions for advanced analytics 2. Complex JOIN operations and optimizations 3. Merge, update, and delete operations 4. Data type conversions and casting
Advanced Snowflake Features	10-15%	- External Integrations 1. Snowflake Connector for Python/Java 2. External functions and UDFs 3. Partner integrations (Spark, Kafka, etc.) - Data Lakes 1. External tables and Iceberg support 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 3. Snowflake as a Data Lake - Data Sharing 1. Reader accounts 2. Direct sharing vs Data Marketplace 3. Secure share creation and management
Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Table types (permanent, transient, temporary) 2. Micro-partitioning concepts 3. Hybrid and Hyper tables 4. Clustering strategies and key selection - Performance Optimization 1. Warehouse scaling policies 2. Query profiling and execution plans 3. Materialized views and cached results 4. Result set caching

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
An online retailer uses multiple delivery partners to deliver products to customers. The delivery partners send order summaries to the retailer. The retailer stores the order summaries in Amazon S3.
Some of the order summaries contain personally identifiable information (PII) about customers. A data engineer needs to detect PII in the order summaries so the company can redact the PII.
Which solution will meet these requirements with the LEAST operational overhead?

A. Amazon Textract
B. Amazon SageMaker Data Wrangler
C. Amazon Macie
D. Amazon S3 Storage Lens

Answer: C

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover and protect sensitive data, such as Personally Identifiable Information (PII), stored in Amazon S3. Macie can automatically scan the order summaries for PII, enabling the company to detect and then redact PII as required.

NEW QUESTION # 45
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. MOVE city, state FROM cities_world TO cities_usa WHERE country='usa';
B. INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country='usa';
C. INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country='usa';
D. UPDATE cities_usa SET (city, state) = (SELECT city, state FROM cities_world WHERE country='usa');

Answer: B

NEW QUESTION # 46
A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?

A. Rewrite the report to eliminate the use of the loop construct
B. Increase the number of maximum clusters of the virtual warehouse
C. Define a clustering key for the sales data table
D. Increase the size of the virtual warehouse

Answer: A

Explanation:
Explanation
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake's parallel processing and optimization capabilities and reduce the network overhead and latency.

NEW QUESTION # 47
The developer team is using AWS CodeCommit for hosting private Git repositories. Developers review the code stored in this repository and merge it to the branches. The developer team wants to download code contents from a remote branch to a local repository.
What command can be used for this purpose?

A. git branch -a
B. git fetchright
C. git branch -r
D. git branch

Answer: B

Explanation:
The "git fetch" command will pull information about all the branches in the CodeCommit repository to the local repo.
Reference:
https://docs.aws.amazon.com/codecommit/latest/userguide/how-to-basic-git.html

NEW QUESTION # 48
Search optimization works best to improve the performance of a query when the following condi-tions are true:[Select All that apply]

A. The table is frequently queried on columns other than the primary cluster key.
B. Search Query uses Sort Operations.
C. The table is not clustered.
D. Search Query uses Equality predicates (for example, <column_name> = <constant>) OR Predicates that use IN.

Answer: A,C,D

Explanation:
Explanation
Materialized Views works best for search query performance in case of Sort Operations. For Rest of the points Search optimization works best to improve query performance.

NEW QUESTION # 49
......

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