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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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.
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.
Topic 4	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 5	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
Find out the odd one out:

A. 1. Bulk Data Load: Billed for the amount of time each virtual warehouse is active.
2. SnowPipe: Billed according to the compute resources used in the Snowpipe ware-house while loading the files.
B. 1. Bulk Data Load: Requires a user-specified warehouse to execute COPY statements.
2. SnowPipe: Uses Snowflake-supplied compute resources.
C. 1. Bulk Data Load: Loads are always performed in a single transaction.
2. SnowPipe: Loads are combined or split into a single or multiple transactions based on the number and size of the rows in each data file.
D. 1. Bulk Data Load: Load history Stored in the metadata of the target table for 365 days.
2. SnowPipe: Load history Stored in the metadata of the pipe for 64 days.

Answer: D

Explanation:
Explanation
Bulk data load
Load History Stored in the metadata of the target table for 64 days. Available upon completion of the COPY statement as the statement output.
Snowpipe
Load History Stored in the metadata of the pipe for 14 days. Must be requested from Snowflake via a REST endpoint, SQL table function, or ACCOUNT_USAGE view.
Rest are correct statements.

NEW QUESTION # 171
A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables.
Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?

A. Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables.
Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
B. Use the AWS Management Console to edit the rules within the Data Catalog.
C. Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
D. Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.

Answer: C

NEW QUESTION # 172
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?

A. CREATE TABLE new_table
as SELECT *
FROM old_cable
WHERE 1=1;
B. CREATE TABLE new_table
AS SELECT *
FROM old_table
WITH NO DATA;
C. CREATE TABLE new_table
LIKE old_table;
D. CREATE TABLE new_table
AS SELECT *
FROM old_table;

Answer: B

NEW QUESTION # 173
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

A. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
C. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.

Answer: D

NEW QUESTION # 174
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?

A. Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.
B. Write a PySpark ETL script. Host the script on an Amazon EMR cluster.
C. Write an AWS Glue Python shell job. Use pandas to transform the data.
D. Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

Answer: D

Explanation:
AWS Glue Python Shell Job is billed $0.44 per DPU-Hour for each job.
AWS Glue PySpark is billed $0.29 per DPU-Hour for each job with flexible execution and $0.44 per DPU-Hour for each job with standard execution.
https://aws.amazon.com/glue/pricing/

NEW QUESTION # 175
......

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