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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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.
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| Topic 4 | - 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.
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| Topic 5 | - 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.
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Snowflake DEA-C01 Web-Based Practice Exam Questions
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q219-Q224):
NEW QUESTION # 219
Search optimization works best to improve the performance of a query when the following condi-tions are true:[Select All that apply]
- A. Search Query uses Equality predicates (for example, <column_name> = <constant>) OR Predicates that use IN.
- B. The table is frequently queried on columns other than the primary cluster key.
- C. Search Query uses Sort Operations.
- D. The table is not clustered.
Answer: A,B,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 # 220
A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses.
The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds.
All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline.
Which solution will meet these requirements?
- A. Increase the number of retries by using the Maximum Retry Attempts setting.
- B. Increase the memory of the Lambda functions to 512 MB.
- C. Increase the timeout value for the Lambda functions from 15 seconds to 30 seconds.
- D. Configure the Lambda functions to run in the company's VPC.
Answer: B
Explanation:
In AWS Lambda, increasing memory also proportionally increases the allocated CPU. By raising the Lambda functions' memory from 128 MB to 512 MB, the functions receive more CPU power, which reduces execution time and improves performance without changing logic or timeouts.
NEW QUESTION # 221
A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data.
Which solution will meet these requirements?
- A. Use AWS Glue DataBrew to create the partitions for the AWS Glue table.
- B. Configure an AWS Glue crawler to run on a set schedule.
- C. Set partition projection properties for the AWS Glue table.
- D. Use an AWS Lambda function to create the partitions for the AWS Glue table.
Answer: C
Explanation:
Partition projection in AWS Glue lets Athena automatically calculate partitions based on table properties without explicitly creating them each day. This eliminates the need for crawlers or Lambda jobs, reduces operational overhead, and adapts to schema changes while supporting year, month, and day partitions efficiently.
NEW QUESTION # 222
A company built a data lake and a data warehouse on AWS. The company wants to implement a data catalog to enhance the current data storage solutions. The company wants to have the capability to add business metadata and glossary information to the data catalog for every asset.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon OpenSearch Service. Create an index for the business glossary. Create a second index for the business metadata. Use the OpenSearch Service dashboard to access the metadata.
- B. Use Amazon DataZone. Create the business glossaries. Create metadata forms. Use the Amazon DataZone data portal to access the metadata.
- C. Use an Apache Hive metastore. Create a user table for the business glossary. Use the ALTER TABLE command to change table properties to add business metadata. Create a web application to access the metadata.
- D. Use AWS Glue Catalog. Create a user table for the business glossary. Use the AWS Glue API to change table properties to add business metadata. Create a web application to access the metadata.
Answer: B
Explanation:
Amazon DataZone provides a fully managed data catalog and governance service with built-in support for business glossaries, custom metadata forms, and a user-friendly data portal. You can onboard your existing data assets, define glossaries, and capture business metadata without building or maintaining custom tables, APIs, or applications, minimizing operational overhead.
NEW QUESTION # 223
An application consumes messages from an Amazon Simple Queue Service (Amazon SQS) queue. The application experiences occasional downtime. As a result of the downtime, messages within the queue expire and are deleted after 1 day. The message deletions cause data loss for the application.
Which solutions will minimize data loss for the application? (Choose two.)
- A. Reduce message processing time.
- B. Use a delay queue to delay message delivery
- C. Increase the message retention period
- D. Increase the visibility timeout.
- E. Attach a dead-letter queue (DLQ) to the SQS queue.
Answer: C,E
NEW QUESTION # 224
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