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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Japanese |
| Exam Price: | $375 USD |
| Passing Score: | 750 / 1000 |
| Related Certifications: | SnowPro Advanced: Data Analyst SnowPro Advanced: Security Engineer SnowPro Core SnowPro Advanced: Administrator SnowPro Advanced: Architect SnowPro Advanced: Data Scientist |
| Exam Duration: | 115 minutes |
| Exam Format: | Multiple choice, Multiple select |
| Real Exam Qty: | 65 |
| Recommended Training: | Snowflake University Training Courses SnowPro Advanced: Data Engineer Exam Study Guide |
| Exam Registration: | Snowflake Certification Portal Pearson VUE Registration |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/ |
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NEW QUESTION # 321
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.
Answer: C
NEW QUESTION # 322
A data engineer needs a fully automated solution to check for new data in multiple databases and process data that the solution finds. The solution must run every hour. The solution must be compatible with Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. The solution must be able to process up to 10 MB of data at one time. The solution must be optimized for costs and operational overhead. The solution must have robust error handling capabilities.
Which solution will meet these requirements?
Answer: B
Explanation:
Amazon EventBridge can invoke AWS Step Functions on an hourly schedule, and Step Functions is a serverless orchestration service that is well suited for coordinating checks across multiple data sources while providing built-in retry and catch mechanisms for robust error handling. AWS Lambda can connect to Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service through AWS SDKs, and Lambda can handle workloads well beyond 10 MB in memory, so this design meets the processing-size requirement with low cost and minimal operational overhead compared with EMR or MWAA.
Reference:
https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-handling-error-conditions.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/configuration- samples.html
https://docs.aws.amazon.com/lambda/latest/dg/configuration-memory.html
https://docs.aws.amazon.com/step-functions/latest/dg/workflow-studio-process-error.html
NEW QUESTION # 323
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?
Answer: C
Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html
NEW QUESTION # 324
As a Data Engineer, you have requirement to query most recent data from the Large Dataset that reside in the external cloud storage, how would you design your data pipelines keeping in mind fastest time to delivery?
Answer: B
Explanation:
Explanation
In a typical table, the data is stored in the database; however, in an external table, the data is stored in files in an external stage. External tables store file-level metadata about the data files, such as the filename, a version identifier and related properties. This enables querying data stored in files in an external stage as if it were inside a database. External tables can access data stored in any format supported by COPY INTO <table> statements.
External tables are read-only, therefore no DML operations can be performed on them; however, external tables can be used for query and join operations. Views can be created against external ta-bles.
Querying data stored external to the database is likely to be slower than querying native database tables; however, materialized views based on external tables can improve query performance.
Creating External tables enable user for querying existing data stored in external cloud storage for analysis without first loading it into Snowflake. The source of truth for the data remains in the ex-ternal cloud storage.
Data sets materialized in Snowflake via materialized views are read-only.
This solution is especially beneficial to accounts that have a large amount of data stored in external cloud storage and only want to query a portion of the data; for example, the most recent data. Users can create materialized views on subsets of this data for improved query performance.
NEW QUESTION # 325
Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e.
partitioning the external table). Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.
Answer: C
Explanation:
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.
NEW QUESTION # 326
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