SnowPro Advanced Certified Official Practice Test DEA-C01 – Jan-2025 [Q42-Q57]

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SnowPro Advanced Certified Official Practice Test DEA-C01 – Jan-2025

Ace Snowflake DEA-C01 Certification with Actual Questions Jan 23, 2025 Updated

Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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.

NEW QUESTION 42
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

Change the data format from .csv to JSON format. Apply Snappy compression.

Compress the .csv files by using Snappy compression.

Change the data format from .csv to Apache Parquet. Apply Snappy compression.

Compress the .csv files by using gzip compression.

NEW QUESTION 43
Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused. How Robert in this case, Resume this stale pipe?

PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.

He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.

Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.

select sys-tem$pipe_force_resume(‘mydb.myschema.stalepipe’,’staleness_check_override, ownership_transfer_check_override’);

ALTER PIPES … RESUME statement will resume the pipe.

NEW QUESTION 44
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats.
The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

AWS DataSync

AWS Glue

AWS Direct Connect

Amazon S3 Transfer Acceleration

NEW QUESTION 45
A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud- based environment?

AWS Data Exchange

Amazon Simple Workflow Service (Amazon SWF)

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

AWS Glue

NEW QUESTION 46
Which property can be used with ALTER USER command to temporarily disable MFA for the user so that they can log in?

HOURS_TO_BYPASS_MFA

SECS_TO_BYPASS_MFA

MINS_TO_SKIP_MFA

MINS_TO_BYPASS_MFA

NEW QUESTION 47
Which Snowflake objects does the Snowflake Kafka connector use? (Select THREE).

Pipe

Serverless task

Internal user stage

Internal table stage

Internal named stage

Storage integration

NEW QUESTION 48
What are Common Query Problems a Data Engineer can identified using Query Profiler?

“Exploding” Joins i.e Joins resulting due to a “Cartesian product”

Queries Too Large to Fit in Memory

Inefficient Pruning

Ineffective Data Sharing

NEW QUESTION 49
By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?

TRUE

FALSE

NEW QUESTION 50
What is the purpose of the BUILD_FILE_URL function in Snowflake?

It generates an encrypted URL foe accessing a file in a stage.

It generates a staged URL for accessing a file in a stage.

It generates a permanent URL for accessing files in a stage.

It generates a temporary URL for accessing a file in a stage.

NEW QUESTION 51
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?

Data Hub

Data Vault

Star Schema

Snowflake Schema

NEW QUESTION 52
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)

For daily incoming data, use AWS Glue crawlers to scan and identify the schema.

For daily incoming data, use Amazon Athena to scan and identify the schema.

For daily incoming data, use Amazon Redshift to perform transformations.

For daily and archived data, use Amazon EMR to perform data transformations.

For archived data, use Amazon SageMaker to perform data transformations.

NEW QUESTION 53
As part of Table Designing, Data Engineer added a timestamp column that inserts the current timestamp as the default value as records are loaded into a table. The intent is to capture the time when eachrecord was loaded into the table; however, the timestamps are earlier than the LOAD_TIME column values returned by COPY_HISTORY view (Account Usage). What could be reason of this issue?

LOAD_TIME column values returned by COPY_HISTORY view (Account Usage) gives the same time as returned by CURRENT_TIMESTAMP.

CURRENT_TIMESTAMP values might be different due to query gets executed in warehouse located in different region.

It might be possible that Cloud Provider hosted on Snowflake belongs to region having server time zone lagging Cluster time zone of warehouse where queries get processed & committed.

The reason is, CURRENT_TIMESTAMP is evaluated when the load operation is com-piled in cloud services rather than when the record is inserted into the table (i.e. when the transaction for the load operation is committed).

NEW QUESTION 54
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket.
The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

Use AWS Glue to transform the data for each application. Create multiple copies of the dataset.
Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

NEW QUESTION 55
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning.
The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.

Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.

Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.
Schedule lower capacity during off-peak times.

Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

NEW QUESTION 56
For SQL UDFs, The invoker of the function need not have access to the objects referenced in the function definition, but only needs the privilege to use the function?

TRUE

FALSE

NEW QUESTION 57
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]

Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.

Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).

Snowflake supports resizing a warehouse at any time, even while running.

Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.

Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.

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Snowflake DEA-C01 Exam Syllabus Topics:

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