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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q143-Q148):

NEW QUESTION # 143
Harry using Snowflake Enterprise Edition & decided to scale in/out the Cluster in automatic mode. He needs to configure some warehouses as multi cluster mode and some among them in Standard mode as per needs.
If Harry is using Snowflake Enterprise Edition (or a higher edition), all his warehouses should be configured as multi-cluster warehouses only.

Answer: A

Explanation:
Explanation
If you are using Snowflake Enterprise Edition (or a higher edition), all your warehouses should be configured as multi-cluster warehouses.


NEW QUESTION # 144
A Data Engineer has created table t1 with datatype VARIANT:
create or replace table t1 (cl variant);
The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:

The Engineer now wants to query that data set so that results are shown as normal structured data. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.
The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2; Which select command will produce the correct results?

Answer: B


NEW QUESTION # 145
A company needs to optimize storage costs for an Amazon S3 bucket. The S3 bucket receives
10 million objects every day. The objects range in size from 2 KB to 5 MB. The objects need to be immediately accessible for the first 60 days. Users access objects infrequently from 61 to 180 days. The objects must be accessible within an hour from 181 to 365 days. The company can delete the objects after 365 days. Which solution will meet these requirements?

Answer: C

Explanation:
An S3 Lifecycle policy provides native, automatic tiering by object age without operational overhead, which is critical at 10 million new objects per day. Keeping objects in S3 Standard for the first 60 days satisfies immediate access. Transitioning to S3 Standard-IA from days 61-180 matches infrequent access with millisecond retrieval. Transitioning to S3 Glacier Flexible Retrieval after 180 days meets the "accessible within an hour" requirement. Expiring objects after
365 days satisfies the deletion requirement.


NEW QUESTION # 146
Which use case would be BEST suited for the search optimization service?

Answer: B

Explanation:
Explanation
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.


NEW QUESTION # 147
A company is developing a new application in which vast amounts of data will be ingested in near real-time from Amazon Kinesis Firehose to Amazon S3. AWS Glue will perform ETL on this data set. Files in the Amazon S3 bucket have varying sizes from 50 MB to 1 GB. AWS Glue should be able to query this data with an optimized response time and should support data growth without any performance impact.
What step can be initiated to support this requirement?

Answer: D

Explanation:
Querying vast amounts of data can result in slower response time. A partition index can be used to mediate this problem. While creating the partition index, a list of partition keys from the existing table is specified. Partition Index can be created with any combination of the keys in the table.
With the creation of a partition index, while querying the table, only a subset of the data about the query criteria is scanned. This improves query performance and can be useful to ensure performance for growing data sets.
Reference:
https://docs.aws.amazon.com/whitepapers/latest/aws-glue-best-practices-build-performant-data-pipeline/building-a-performance-efficient-data-pipeline.html
https://docs.aws.amazon.com/glue/latest/dg/partition-indexes.html


NEW QUESTION # 148
......

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