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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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.
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.
Topic 5	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q213-Q218):

NEW QUESTION # 213
A media company wants to build a real-time analytics pipeline to process customer activity events across the company's website and mobile app. The company wants to build a solution to ingest millions of events with minimum latency. The solution must be scalable and durable enough so that no data is lost. Which solution will meet these requirements in the MOST cost-effective way?

A. Deploy an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. Use self- managed consumers to process and distribute data in real-time. Integrate with Amazon Redshift for enhanced analytics.
B. Configure Amazon S3 Event Notifications to invoke an AWS Lambda function to process every new interaction log file. Store the result in Amazon Redshift for analytics.
C. Schedule an AWS Glue job to fetch user interaction logs every 10 minutes from Amazon S3.
Configure the AWS Glue job to transform and store the data in Amazon Redshift for analytics.
D. Set up an Amazon Kinesis Data Streams pipeline to ingest data, process the data by using AWS Lambda functions, and store the results in Amazon Redshift for analytics.

Answer: D

Explanation:
Amazon Kinesis Data Streams is built for durable, scalable, real-time ingestion of streaming events and can process data with very low latency at high volume. AWS documents that Kinesis Data Streams supports real-time stream processing and integrates directly with Amazon Redshift for streaming ingestion, which fits a pipeline handling millions of events without waiting for batch intervals. Using AWS Lambda for stream processing keeps the solution managed and cost- effective, while Amazon MSK would introduce higher cluster cost and operational complexity for this requirement.
Reference:
https://docs.aws.amazon.com/streams/latest/dev/introduction.html

NEW QUESTION # 214
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?

A. Calculate the clustering depth for a table using mentioned columns in the table.
B. Calculate the clustering depth for a table using the clustering key defined for the table.
C. An error: this function does not accept predicates ('C9 = 30') as parameter.
D. An error: this function does not accept lists of columns as a third parameter.

Answer: A

NEW QUESTION # 215
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

A. The maximum concurrency for the AWS Glue job is set to 1.
B. The AWS Glue job does not have a required commit statement.
C. The data engineer incorrectly specified an older version of AWS Glue for the Glue job.
D. The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.

Answer: B

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data

NEW QUESTION # 216
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.
B. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
C. Use Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

Answer: A

Explanation:
By using the built-in transformation and format conversion features of Kinesis Data Firehose, you achieve the desired result with minimal custom development, thereby meeting the requirements efficiently and cost-effectively.

NEW QUESTION # 217
When created, a stream logically takes an initial snapshot of every row in the source object and the contents of a stream change as DML statements execute on the source table.
A Data Engineer, Sophie Created a view that queries the table and returns the CURRENT_USER and CURRENT_TIMESTAMP values for the query transaction. A Stream has been created on views to capture CDC.
Tony, another user inserted the data e.g.
insert into <table> values (1),(2),(3);
Emily, another user also inserted the data e.g.
insert into <table> values (4),(5),(6);
What will happened when Different user queries the same stream after 1 hour?

A. All the Six Records would be displayed with CURRENT_USER & CUR-RENT_TIMESTAMP while querying Streams.
B. User would be displayed with the one who queried during the session, but Recorded timestamp would be of past 1 hour i.e. actual records insertion time.
C. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.
D. All the 6 records would be shown with METADATA$ACTION as 'INSERT' out of which 3 records would be displayed with username 'Tony' & rest 3 records would be displayed with username 'Emily'.

Answer: A

Explanation:
Explanation
When User queries the stream, the stream returns the username for the user. The stream also returns the current timestamp for the query transaction in each row, NOT the timestamp when each row was inserted.

NEW QUESTION # 218
......

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