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Exam DEA-C01 Blueprint & DEA-C01 Reliable Test Labs

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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

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 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

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.

>> Exam DEA-C01 Blueprint <<

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In this cut-throat competitive world of Snowflake, the Snowflake DEA-C01 certification is the most desired one. But what creates an obstacle in the way of the aspirants of the SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) certificate is their failure to find up-to-date, unique, and reliable SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) practice material to succeed in passing the Snowflake DEA-C01 certification exam.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q176-Q181):

NEW QUESTION # 176
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

A. Use AWS Glue DataBrew recipes to read and transform the CSV files.
B. Use an AWS Glue custom crawler to read and transform the CSV files.
C. Use AWS Glue Python jobs to read and transform the CSV files.
D. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.

Answer: A

Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks.
While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew.
AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns.
Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.

NEW QUESTION # 177
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
B. Use Amazon AppFlow to create a flow for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
C. Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.
D. Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

Answer: B

Explanation:
Amazon AppFlow provides fully managed, no-code connectors to a broad range of SaaS applications. By scheduling flows that land data in S3, you avoid building and maintaining custom API-calling code. Glue crawlers can catalog the files and make them available to QuickSight, giving you an end- to-end pipeline with minimal operational effort.

NEW QUESTION # 178
A company manages an Amazon Redshift data warehouse. The data warehouse is in a public subnet inside a custom VPC. A security group allows only traffic from within itself. An ACL is open to all traffic.
The company wants to generate several visualizations in Amazon QuickSight for an upcoming sales event. The company will run QuickSight Enterprise edition in a second AWS account inside a public subnet within a second custom VPC. The new public subnet has a security group that allows outbound traffic to the existing Redshift cluster.
A data engineer needs to establish connections between Amazon Redshift and QuickSight.
QuickSight must refresh dashboards by querying the Redshift cluster.
Which solution will meet these requirements?

A. Create a QuickSight gateway endpoint in the Redshift VPC. Attach an endpoint policy to the gateway endpoint to ensure only specific QuickSight accounts can use the endpoint.
B. Confirm that the CIDR ranges of the Redshift VPC and the QuickSight VPC are the same. If CIDR ranges are different, reconfigure one CIDR range to match the other. Establish network peering between the VPCs.
C. Configure the Redshift security group to allow inbound traffic on the Redshift port from the QuickSight security group.
D. Assign Elastic IP addresses to the QuickSight visualizations. Configure the QuickSight security group to allow inbound traffic on the Redshift port from the Elastic IP addresses.

Answer: C

Explanation:
Because your Redshift cluster is publicly accessible in its VPC and QuickSight runs in a separate VPC and account, you simply need to allow QuickSight's outbound connections through the Redshift security group. By adding an inbound rule on the Redshift SG that permits traffic on port
5439 (or your cluster's port) from the QuickSight security group, QuickSight can refresh dashboards without any additional infrastructure changes.

NEW QUESTION # 179
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
B. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
C. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html

NEW QUESTION # 180
A company runs multiple applications on AWS. The company configured each application to output logs. The company wants to query and visualize the application logs in near real time.
Which solution will meet these requirements?

A. Update the application code to send the log data to Amazon QuickSight by using Super-fast, Parallel, In-memory Calculation Engine (SPICE).Create the required analyses and dashboards in QuickSight.
B. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an Amazon S3 bucket. Create an AWS Lambda function that runs on a schedule to export the required log groups to the S3 bucket. Use Amazon Athena to query the log data in the S3 bucket.
C. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Use CloudWatch log anomaly detection to query and visualize the log data.
D. Create an Amazon OpenSearch Service domain. Configure the applications to output logs to Amazon CloudWatch Logs log groups. Create an OpenSearch Service subscription filter for each log group to stream the data to OpenSearch. Create the required queries and dashboards in OpenSearch Service to analyze and visualize the data.

Answer: D

Explanation:
By streaming your CloudWatch Logs log groups directly into an Amazon OpenSearch Service domain via subscription filters, you get near-real-time indexing and can build Kibana dashboards for querying and visualization with virtually no batching delays. This fully managed pipeline minimizes custom glue code and meets your requirements with low operational overhead.

NEW QUESTION # 181
......

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