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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Engineering Fundamentals on Snowflake	- Data ingestion concepts 1. Batch loading strategies 2. Continuous ingestion concepts - Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation
Topic 2: Performance Optimization	- Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms - Warehouse optimization 1. Scaling strategies 2. Concurrency management
Topic 3: Data Pipelines and Transformation	- ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts - Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks
Topic 4: Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Flattening nested data 2. Complex transformations in SQL - Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage
Topic 5: Data Loading and Integration	- Snowpipe and continuous ingestion 1. Automated ingestion workflows 2. Event-driven loading - Bulk data loading 1. COPY INTO command usage 2. File format handling
Topic 6: Security, Governance, and Data Sharing	- Access control 1. Role-based access control (RBAC) 2. Object-level permissions - Data governance 1. Secure data sharing concepts 2. Data masking policies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q255-Q260):

NEW QUESTION # 255
Mark the Correct Statements:
Statement 1. Enable failover for a primary database to one or more accounts in your organization using an ALTER DATABASE ... ENABLE FAILOVER TO ACCOUNTS statement.
Statement 2. Enabling failover for a primary database can be done by Data Engineer either before or after a replica of the primary database has been created in a specified account.

A. Statement 1
B. Both are Correct.
C. Both are False.
D. Statement 2

Answer: B

NEW QUESTION # 256
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?

A. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
(Correct)
B. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
C. A materialized view speeds both equality searches and range searches.
D. She can go for clustering on underlying tables which can speedup Equality searches.

Answer: A

Explanation:
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.

NEW QUESTION # 257
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

A. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
B. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.
C. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
Update the network connections to interact with the newly deployed EC2 instances.
D. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.

Answer: D

Explanation:
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.

NEW QUESTION # 258
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications?
(Choose two.)

A. ANSI SQL
B. Gremlin
C. Spark SQL
D. SQL
E. SPARQL

Answer: B,E

NEW QUESTION # 259
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Map state
B. Choice state
C. Parallel state
D. Wait state

Answer: A

Explanation:
Map state is designed precisely for the requirement described. It allows you to iterate over a collection of items, processing each item individually. The Map state can automatically manage the iteration and execute the specified transformation on each item in parallel, making it the perfect choice for parallel processing of a large collection of data files.

NEW QUESTION # 260
......

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