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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Certificate Validity Period: | 2 years |
| Exam Price: | $375 USD |
| Related Certifications: | SnowPro Advanced: Data Scientist SnowPro Advanced: Administrator SnowPro Advanced: Security Engineer SnowPro Advanced: Architect SnowPro Core SnowPro Advanced: Data Analyst |
| Real Exam Qty: | 65 |
| Exam Duration: | 115 minutes |
| Passing Score: | 750 / 1000 |
| Exam Format: | Multiple select, Multiple choice |
| Available Languages: | English, Japanese |
| Recommended Training: | Snowflake University Training Courses SnowPro Advanced: Data Engineer Exam Study Guide |
| Exam Registration: | Pearson VUE Registration Snowflake Certification Portal |
| 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/ |
>> Snowflake DEA-C01 Exam Objectives <<
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NEW QUESTION # 179
A multinational corporation operating across various sectors has deployed its data infrastructure on AWS, utilizing Amazon S3 for object storage, Amazon Redshift for data warehousing, and Amazon RDS for relational databases. The data analyst team, responsible for extracting insights from these diverse data sources, seeks a solution to query and analyze data across all platforms efficiently. They aim for a unified querying platform that seamlessly integrates with different data stores, allowing for holistic analysis without the overhead of data movement or complex transformations.
Which of the following approaches will meet the requirements of the data analyst team with the least operational effort and most cost-friendly? (Select TWO)
Answer: B,D
Explanation:
AWS Glue Data Catalog can be used to create a centralized metadata repository, providing a unified view of the data across different data sources. This approach requires minimal operational effort as it involves creating and managing metadata, and it is cost-friendly compared to other options. Amazon Athena allows federated queries to directly query data stored in respective data sources using standard SQL. It provides a serverless solution for querying data without the need for data movement, resulting in minimal operational effort and cost.
References:
https://aws.amazon.com/glue/features/catalog/
https://aws.amazon.com/athena/
https://aws.amazon.com/redshift/spectrum/
https://aws.amazon.com/blogs/aws/new-query-your-data-lake-with-amazon-aurora-federated-query/
https://aws.amazon.com/emr/
NEW QUESTION # 180
A company is using Amazon Redshift as a data warehousing service. It is running analytical queries on large amounts of data using ODBC connections. The operations team is observing that some queries are getting completed while others are getting hung. They are seeking your suggestions to resolve this issue.
What setting can be done?
Answer: C
Explanation:
Maximum Transmission Unit (MTU) determines the maximum size in packet bytes that can be transferred in a single Ethernet frame between two hosts.
Customers may face query hang issues when the MTU size in a network path between two IP hosts is mismatched. To resolve the query hang issue for Redshift ODBC connections, the MTU size can be reduced.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/queries-troubleshooting.html#queries-troubleshooting-query-hangs
https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-drop-issues.html
NEW QUESTION # 181
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?
Answer: D
Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.
NEW QUESTION # 182
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?
Answer: E,F
Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.
NEW QUESTION # 183
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?
Answer: B,D
Explanation:
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');
NEW QUESTION # 184
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