[Q216-Q237] Free Sample Questions to Practice DEA-C01 Certification Test Engine [Oct-2026]

Rate this post

Free Sample Questions to Practice DEA-C01 Certification Test Engine [Oct-2026]

2026 Valid DEA-C01 Real Exam Questions, practice SnowPro Advanced

Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 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.

NO.216 Data Engineer is using existing pipe that automates data loads using event notifications, later he figured out the needs to modify pipe properties. For the same, He decided to recreate the pipe as best practice. He followed the below steps for the same.
1. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
2. Recreate the pipe (using CREATE OR REPLACE PIPE).
3. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?

CREATE OR REPLACE PIPE command will recreate the PIPE successfully.

Terminate the existing pipe (using ALTER PIPE … SET PIPE_EXECUTION_TERMINATE = true) before recreation.

Pause the pipe (using ALTER PIPE … SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE … SET PIPE_EXECUTION_PAUSED = false).

Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).

NO.217 To troubleshoot data load failure in one of your Copy Statement, Data Engineer have Executed a COPY statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS with reference to the set of files he had attempted to load. Which below function can facilitate analysis of the problematic records on top of the Results produced? [Select 2]

RESULT_SCAN

LAST_QUERY_ID

Rejected_record

LOAD_ERROR

NO.218 A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day’s CSV file.
A data engineer needs to ensure that the previous day’s data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?

Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.

Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required fields and empty files.

Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.

Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.

NO.219 A data engineer uses Amazon Kinesis Data Streams to ingest and process records that contain user behavior data from an application every day.
The data engineer notices that the data stream is experiencing throttling because hot shards receive much more data than other shards in the data stream.
How should the data engineer resolve the throttling issue?

Use a random partition key to distribute the ingested records.

Increase the number of shards in the data stream. Distribute the records across the shards.

Limit the number of records that are sent each second by the producer to match the capacity of the stream.

Decrease the size of the records that the producer sends to match the capacity of the stream.

NO.220 A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.
The company updates the store location table only once or twice every few years.
A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.
Which solution will meet these requirements in the MOST cost-effective way?

Change the distribution style of the store location table from EVEN distribution to ALL distribution.

Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.

Add a join column named store_id into the sort key for all the tables.

Upgrade the Redshift reserved node to a larger instance size in the same instance family.

NO.221 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.

NO.222 When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?

FALSE

TRUE

NO.223 Snowflake does not provide which of following set of SQL functions to support retrieving infor-mation about tasks?

SYSTEM$CURRENT_USER_TASK_NAME

TASK_HISTORY

TASK_DEPENDENTS

TASK_QUERY_HISTORY

SYSTEM$TASK_DEPENDENTS_ENABLE

NO.224 A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.

Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.

Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.

Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function’s alias.

NO.225 A Data Engineer is building a set of reporting tables to analyze consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing Which views are needed MINIMALLY as data sources?

SNOWFLAKE- DATA_SHARING_USAGE – LISTING_EVENTS_BAILY

SNOWFLAKE.DATA_SHARING_USAGE.LISTING_CONSOKE>TION_DAILY

SNOWFLAKE. DATA_SHARING_USAGE. LISTING_TELEMETRY_DAILy

SNOWFLAKE.ACCOUNT_USAGE.DATA _TRANSFER_HISTORY

NO.226 Which Snowflake feature facilitates access to external API services such as geocoders. data transformation, machine Learning models and other custom code?

Security integration

External tables

External functions

Java User-Defined Functions (UDFs)

NO.227 A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster’s logs with the application’s traces to identify points of failure in the whole application request flow.
Which combination of steps will meet these requirements with the LEAST development effort?
(Choose two.)

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.

Use Amazon OpenSearch to correlate the logs and traces.

Use AWS Glue to correlate the logs and traces.

NO.228 A stock broking firm is working on a new application that stores and updates stock market data in Amazon DynamoDB. Different applications will track these changes in real-time. The team is looking for a tool to capture this changing data, which should be available for processing for up to one year. Streaming data should be cleansed, transformed, and stored in Amazon S3 data lakes.
Which service can be considered?

Use DynamoDB Streams as a streaming model. Process stream records with AWS Lambda.

Use DynamoDB Streams as a streaming model. Process stream records with AWS Data Firehose.

Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with AWS Glue streaming ETL.

Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with DynamoDB Streams Kinesis adapter.

NO.229 A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

AWS Step Functions

AWS Glue workflows

AWS Glue Studio

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

NO.230 A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

Use Amazon Redshift ML to generate inventory recommendations.

Use SQL to invoke a remote SageMaker endpoint for prediction.

Use Amazon Redshift ML to schedule regular data exports for offline model training.

Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

Use Amazon Redshift as a file storage system to archive old inventory management reports.

NO.231 Which are the two ways to access elements in a JSON object?

Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>.

use Curly bracket notation to traverse the path in an object:
<col-umn>{‘<level1_element>’}{‘<level2_element>’}.

Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>.

use bracket notation to traverse the path in an object:
<col-umn>[‘<level1_element>’][‘<level2_element>’].

NO.232 A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

Use Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.

Use Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

NO.233 A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user’s selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?

Increase the size of the virtual warehouse

Increase the number of maximum clusters of the virtual warehouse

Define a clustering key for the sales data table

Rewrite the report to eliminate the use of the loop construct

NO.234 Company DEF has a strict security policy that mandates that all data at rest in Amazon S3 must be encrypted. They want to ensure that the encryption keys are managed by AWS, but they also want the flexibility to change the encryption keys when required.
Which of the following encryption methods best meets Company DEF’s requirements?

Server-Side Encryption with Customer-Provided Keys (SSE-C).

Server-Side Encryption with Amazon S3 Managed Keys (SSE-S3).

Server-Side Encryption with AWS Key Management Service (SSE-KMS).

Client-Side Encryption with a client-side master key.

NO.235 A data engineer uploads confidential documents to an Amazon S3 bucket every day. The data engineer requires a solution to independently verify the integrity of all uploaded data to confirm that there was no corruption during the transfer process.
Which solution will meet this requirement?

Download a subset of the data after the data is uploaded to the S3 bucket. Manually validate the objects for integrity.

Change the default encryption on the S3 bucket to server-side encryption with customer-provided keys (SSE-C). Turn on S3 bucket keys to validate data integrity.

Calculate the SHA-256 checksum for the objects before uploading the objects. Pass the calculated value to the AWS SDK in each upload request.

Download the complete data after the data is uploaded to the S3 bucket. Programmatically validate the objects for integrity.

NO.236 A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit. The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.
Use the security configuration during EMR cluster creation.

Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption.
Create the EMR cluster, and attach the security configuration to the cluster.

NO.237 A company runs a multi-tenant Amazon EMR cluster on Amazon EC2 instances. Multiple teams perform interactive query analyses and data transformations on the data in the EMR cluster. The teams can access the cluster only through EMR Studio workspaces and EMR steps.
The teams need to use EMR steps to run Apache Spark jobs to fetch data from an Amazon DynamoDB table. The DynamoDB table contains confidential data that must be accessible to only one specific team. The company needs to ensure that only the appropriate team can access the confidential data in the EMR cluster. Which solution will meet these requirements?

Set up runtime roles for EMR steps.

Set up AWS Lake Formation permissions.

Set up IAM roles for EMR File System (EMRFS) requests.

Set up a DynamoDB resource-based policy.

Genuine DEA-C01 Exam Dumps Free Demo Valid QA’s: https://www.actualcollection.com/DEA-C01-exam-questions.html

Snowflake DEA-C01 Exam Syllabus Topics:

Leave a comment Cancel reply