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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q68-Q73):

NEW QUESTION # 68
A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.
The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.
Which solution will meet these requirements?

A. Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
B. Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.
C. Use an AWS Give job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.
D. Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-single-copy-command.html
https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html

NEW QUESTION # 69
A company is working on a new big data analytical tool that will store data in the EFS regional file system. Workload performance is calculated and will be 10% of the average-to-peak ratio. Data in the file system should be accessed with the lowest latency.
What performance and throughput mode can be selected?

A. Select default performance mode with Provisioned throughput mode.
B. Select Max I/O mode with bursting throughput mode.
C. Select Max I/O mode with elastic throughput mode.
D. Select general purpose mode with bursting throughput mode.

Answer: A

Explanation:
Amazon EFS provides the following performance modes
General Purpose: This is a default performance mode offering the lowest latency.
Max I/O: This mode is suitable for parallelized workloads which can withstand higher latency.
Amazon EFS offers the following throughput modes,
Elastic Throughput Mode: Suitable for spiky or unpredictable workloads or when the application requires throughput at an average-to-peak ratio of 5% or less.
Provisioned Throughput Mode: Suitable for predictable workloads or when the application requires throughput at an average-to-peak ratio of 5% or more.
Bursting Throughout Mode: Suitable when throughput needs to be scaled to the amount of storage in the file system.
In the above case, to achieve the lowest latency and throughput at 10% of the average-to-peak ratio, the default performance modes, which are the General purpose mode and the Provisioned throughput mode, can be selected.
Reference:
https://docs.aws.amazon.com/efs/latest/ug/performance.html

NEW QUESTION # 70
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?

A. REST endpoints --> ingestfiles
B. REST endpoints --> loadHistoryScan
C. REST endpoints --> insertReport
D. REST endpoints--> insertfiles

Answer: D

Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion

NEW QUESTION # 71
A media company wants to improve a system that recommends media content to customers based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from a Git repository.
C. Use API calls to access and integrate third-party datasets from AWS DataSync.
D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Answer: A

Explanation:
AWS Data Exchange is designed to quickly subscribe to and consume third-party datasets through a managed service interface and APIs, minimizing the integration effort and operational overhead compared to building custom ingestion from other sources.

NEW QUESTION # 72
Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)

A. DataFrame.col ( )
B. DataFrame.collect ()
C. DataFrame.show ()
D. DateFrame.select ()
E. DataFrame.random_split ( )

Answer: B,C

Explanation:
Explanation
The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.

NEW QUESTION # 73
......

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