Updated Snowflake DEA-C01 Practice Questions In Three Formats

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	Approximately 65 questions
Exam Format:	Scenario-based questions, Multiple select, Multiple choice
Available Languages:	English
Exam Duration:	115 minutes
Related Certifications:	SnowPro Core Certification
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Recommended Training:	Snowflake University Training SnowPro Advanced Data Engineer Exam Guide
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 2	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 3	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 4	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 5	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q276-Q281):

NEW QUESTION # 276
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

A. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
B. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
C. Change the distribution key to the table column that has the largest dimension.
D. Upgrade the reserved node from ra3.4xlarge to ra3.16xlarge.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/t_Distributing_data.html

NEW QUESTION # 277
Mark the correct statements about Cache?

A. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
B. Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.
C. The size of the warehouse cache is determined by the compute resources in the ware-house.
D. Materialized views are more flexible than, but typically slower than, cached results.
E. For persisted query results of all sizes, the cache expires after 24 hours.

Answer: A,B,C,D,E

Explanation:
Explanation
How Does Warehouse Caching Impact Queries?
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse. This enables improved performance for subsequent queries if they are able to read from the cache instead of from the table(s) in the query. The size of the cache is determined by the compute resources in the warehouse (i.e. the larger the warehouse and, therefore, more compute re-sources in the warehouse), the larger the cache.
This cache is dropped when the warehouse is suspended, which may result in slower initial perfor-mance for some queries after the warehouse is resumed. As the resumed warehouse runs and pro-cesses more queries, the cache is rebuilt, and queries that are able to take advantage of the cache will experience improved performance.
Keep this in mind when deciding whether to suspend a warehouse or leave it running. In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the cache of data from previous queries to help with performance.
Using Persisted Query Results
When a query is executed, the result is persisted (i.e. cached) for a period of time. At the end of the time period, the result is purged from the system.
Snowflake uses persisted query results to avoid re-generating results when nothing has changed (i.e. "retrieval optimization"). In addition, you can use persisted query results to post-process the results (e.g. layering a new query on top of the results already calculated).
For persisted query results of all sizes, the cache expires after 24 hours.
Both materialized views and cached query results provide query performance benefits:
Materialized views are more flexible than, but typically slower than, cached results.
Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
Regular views do not cache data, and therefore cannot improve performance by caching.

NEW QUESTION # 278
You are tasked with optimizing real-time data processing for a large e-commerce platform using Amazon Managed Service for Flink. During testing, you observe intermittent latency spikes in data ingestion and processing. What is the most likely cause of this issue?

A. Inefficient keying and partitioning strategy in the Flink applicationright
B. Outdated version of Amazon Managed Service for Flink
C. Excessive load on the Amazon S3 storage used for checkpointing and state management
D. Insufficient network bandwidth between data sources and Amazon Managed Service for Flink

Answer: A

Explanation:
Inefficient keying and partitioning strategies directly affect the distribution of workload across Flink tasks. Uneven workload distribution can lead to certain tasks processing more data than others, causing delays and intermittent latency spikes. Optimizing keying and partitioning strategies within the Flink application can help distribute the workload evenly and reduce processing latency.
Reference:
https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/dev/datastream/fault-tolerance/state/#keyed-state-and-partitioning

NEW QUESTION # 279
Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket.
Which solution will meet these requirements?

A. Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.
B. Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.
C. Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.
D. Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

Answer: C

NEW QUESTION # 280
An ecommerce company operates a complex order fulfilment process that spans several operational systems hosted in AWS. Each of the operational systems has a Java Database Connectivity (JDBC)-compliant relational database where the latest processing state is captured.
The company needs to give an operations team the ability to track orders on an hourly basis across the entire fulfillment process.
Which solution will meet these requirements with the LEAST development overhead?

A. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon Redshift Build dashboards in Amazon QuickSight that track the orders.
B. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Grafana dashboards that track the orders.
C. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon DynamoDBuild dashboards in Amazon QuickSight that track the orders.
D. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Amazon QuickSight dashboards that track the orders.

Answer: A

NEW QUESTION # 281
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