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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation & Processing	30%	- Implement data quality, validation, and schema evolution - Work with semi-structured and unstructured data - Use streams, tasks, dynamic tables for pipelines and CDC - Design and implement transformations: SQL, stored procedures, user-defined functions
Data Pipeline Orchestration & Automation	20%	- Integrate with external orchestration tools - Error handling, retry logic, and dependency management - Build, schedule, monitor, and manage workflows
Performance Optimization & Scalability	15%	- Optimize warehouses, clustering, partitioning, and query performance - Monitor and tune workloads and resource usage - Design scalable compute and storage solutions
Data Ingestion	25%	- Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Handle file formats, compression, encoding, and schema detection - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming
Data Governance, Security & Sharing	10%	- Track lineage, audit, and maintain data integrity - Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?

A. You have a low, fluctuating workload for the warehouse.
B. You require the warehouse to be available with delay.
C. You have a heavy, steady workload for the warehouse.
D. You require the warehouse to be available with no delay or lag time.

Answer: C,D

Explanation:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.

NEW QUESTION # 147
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?

A. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
B. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
C. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
D. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.

Answer: C

Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.

NEW QUESTION # 148
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?

A. For Data loading of files with semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-16.
B. For Local environment, Files are first copied ("staged") to an internal (Snowflake) stage, then loaded into a table.
C. For loading data from all semi-structured supported file formats (JSON, Avro, etc.), as well as unloading data, UTF-8 is the only supported character set.
D. UTF-32 & UTF-16 both encoding character sets supported for loading data from de-limited files (CSV, TSV, etc.)

Answer: A

Explanation:
Explanation
For Data Loading of delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading.
For semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-8.
Rest of the statements are correct.

NEW QUESTION # 149
A Data Engineer wants to check the status of a pipe named my_pipe. The pipe is inside a database named test and a schema named Extract (case-sensitive).
Which querywill provide the status of the pipe?

A. SELECT FROM SYSTEM$PIPE_STATUS (''test.'extract'.my_pipe"i:
B. SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I
C. SELE2T * FROM SYSTEM$PIPE_STATUS < ' test. "Extract", my_pipe');
D. SELECT * FROM SYSTEM$PIPE_STATUS ("test. 'extract' .my_pipe"};

Answer: C

Explanation:
Explanation
The query that will provide the status of the pipe is SELECT * FROM
SYSTEM$PIPE_STATUS('test."Extract".my_pipe');. The SYSTEM$PIPE_STATUS function returns information about a pipe, such as its name, status, last received message timestamp, etc. The function takes one argument: the pipe name in a qualified form. The pipe name should include the database name, the schema name, and the pipe name, separated by dots. If any of these names are case-sensitive identifiers, they should be enclosed in double quotes. In this case, the schema name Extract is case-sensitive and should be quoted. The other options are incorrect because they do not follow the correct syntax for the pipe name argument. Option A and B use single quotes instead of double quotes for case-sensitive identifiers. Option D uses double quotes instead of single quotes for non-case-sensitive identifiers.

NEW QUESTION # 150
A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization.
The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS).
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
B. Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event- driven scaling of Spark applications.
C. Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
D. Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.

Answer: B

Explanation:
Amazon EMR on EKS is the managed AWS service for running Apache Spark on an existing Amazon EKS environment, so it integrates directly with the company's current Kubernetes infrastructure while reducing operational work compared with running Spark yourself. KEDA is specifically designed for event-driven autoscaling in Kubernetes, which satisfies the requirement for event-driven scaling and efficient resource use. This combination provides the managed Spark runtime plus Kubernetes-native event-based scaling with the least operational overhead.
Reference:
https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/event-driven-auto-scaling-with-eks-pod-identity-and-keda.html

NEW QUESTION # 151
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