Snowflake - DEA-C01 - SnowPro Advanced: Data Engineer Certification Exam–Updated Exam Score

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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	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	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	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 (Q272-Q277):

NEW QUESTION # 272
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?

A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.
B. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume.
Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data.
Apply the default settings to the EC2 instances.
C. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.
D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

Answer: C

Explanation:
Amazon EBS volumes are network-attached, and they persist independently of the life of an EC2 instance. By using an AMI backed by an Amazon EBS volume, the root device for the instance is an EBS volume, which means the data will persist.

NEW QUESTION # 273
SYSTEM$CLUSTERING_INFORMATION functions returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function returns a JSON object containing average_overlaps name/value pairs. Does High average_overlaps indicates well organized Clustering?

A. YES
B. NO

Answer: B

Explanation:
Explanation
Higher the avg_overlap indicates poorly organized clustering.

NEW QUESTION # 274
The COPY command supports several options for loading data files from a stage i.e.
I. By path
II. Specifying a list of specific files to load.
III. Using pattern matching to identify specific files by pattern.
IV. Organize files into logical paths that reflect a scheduling pattern.
Of the aforesaid options for identifying/specifying data files to load from a stage, which option in general is the fastest & best considerate?

A. III
B. IV
C. II
D. I

Answer: C

Explanation:
Explanation
Of the above options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
For example:
copy into load1 from @%load1/Snow1/ files=('mydata1.csv', 'mydata2.csv', 'mydata3.csv')

NEW QUESTION # 275
A Data Engineer wants to check the status of a pipe named my_pipe. The pipe is inside a database named test and a schema named Extract (case-sensitive).
Which querywill provide the status of the pipe?

A. SELE2T * FROM SYSTEM$PIPE_STATUS < ' test. "Extract", my_pipe');
B. SELECT FROM SYSTEM$PIPE_STATUS (''test.'extract'.my_pipe"i:
C. SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I
D. SELECT * FROM SYSTEM$PIPE_STATUS ("test. 'extract' .my_pipe"};

Answer: A

Explanation:
Explanation
The query that will provide the status of the pipe is SELECT * FROM
SYSTEM$PIPE_STATUS('test."Extract".my_pipe');. The SYSTEM$PIPE_STATUS function returns information about a pipe, such as its name, status, last received message timestamp, etc. The function takes one argument: the pipe name in a qualified form. The pipe name should include the database name, the schema name, and the pipe name, separated by dots. If any of these names are case-sensitive identifiers, they should be enclosed in double quotes. In this case, the schema name Extract is case-sensitive and should be quoted. The other options are incorrect because they do not follow the correct syntax for the pipe name argument. Option A and B use single quotes instead of double quotes for case-sensitive identifiers. Option D uses double quotes instead of single quotes for non-case-sensitive identifiers.

NEW QUESTION # 276
A company wants to migrate a data warehouse from Teradata to Amazon Redshift.
Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
C. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
D. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema.
Use AWS DMS to migrate the data.

Answer: B

Explanation:
This solution provides the least operational effort for migrating a data warehouse from Teradata to Amazon Redshift by using AWS services that are specifically designed for schema and data migration:
AWS Schema Conversion Tool (SCT) is used to convert and migrate the schema from Teradata to Amazon Redshift, handling schema differences automatically and reducing manual work.
AWS Database Migration Service (DMS) is then used to transfer the data from Teradata to Redshift, allowing for efficient data migration.

NEW QUESTION # 277
......

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