[Q47-Q69] Use Real DEA-C01 – 100% Cover Real Exam Questions [Sep-2026]

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Use Real DEA-C01 – 100% Cover Real Exam Questions [Sep-2026]

Dumps Brief Outline Of The DEA-C01 Exam – DumpsTorrent

Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Governance and Security	15-20%	– Access Control 1. Role hierarchy and ownership 2. Role-based access control (RBAC) 3. GRANT and REVOKE operations 4. Row-level and column-level security – Data Protection 1. Data masking and tokenization 2. External tokenization 3. Secure data sharing across accounts 4. Data re-identification risks – Compliance and Monitoring 1. Access history and audit trails 2. Tag-based policies 3. Data classification
Data Transformation	20-25%	– SQL-Based Transformations 1. Merge, update, and delete operations 2. Window functions for advanced analytics 3. Complex JOIN operations and optimizations 4. Data type conversions and casting – Streams and Tasks 1. Task graph design and dependencies 2. Change Data Capture (CDC) with streams 3. Serverless task warehouse configuration 4. Scheduled and event-based task execution – Data Processing Patterns 1. ELT vs ETL approaches in Snowflake 2. Time Travel for historical data access 3. Zero Copy Cloning for data provisioning
Advanced Snowflake Features	10-15%	– Data Lakes 1. Snowflake as a Data Lake 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 3. External tables and Iceberg support – Data Sharing 1. Secure share creation and management 2. Direct sharing vs Data Marketplace 3. Reader accounts – External Integrations 1. External functions and UDFs 2. Snowflake Connector for Python/Java 3. Partner integrations (Spark, Kafka, etc.)
Data Storage and Retrieval	15-20%	– Snowflake Architecture 1. Clustering strategies and key selection 2. Micro-partitioning concepts 3. Hybrid and Hyper tables 4. Table types (permanent, transient, temporary) – Performance Optimization 1. Warehouse scaling policies 2. Result set caching 3. Materialized views and cached results 4. Query profiling and execution plans
Performance Engineering	10-15%	– Warehouse Management 1. Resource monitors and cost control 2. Multi-cluster warehouse configuration 3. Auto-suspend and auto-resume settings – Query Optimization 1. Predicate pushdown optimization 2. Query rewrite and optimization techniques 3. Join elimination and simplification
Data Ingestion	15-20%	– Continuous Data Ingestion 1. Serverless compute for Snowpipe 2. Snowpipe configuration and usage 3. Real-time data ingestion patterns – Bulk Data Loading 1. Staging configurations (internal and external stages) 2. COPY INTO command options and best practices 3. File format considerations (CSV, JSON, Parquet, Avro) 4. Data loading error handling and validation

NEW QUESTION 47
An online retailer uses multiple delivery partners to deliver products to customers. The delivery partners send order summaries to the retailer. The retailer stores the order summaries in Amazon S3.
Some of the order summaries contain personally identifiable information (PII) about customers. A data engineer needs to detect PII in the order summaries so the company can redact the PII.
Which solution will meet these requirements with the LEAST operational overhead?

Amazon Textract

Amazon S3 Storage Lens

Amazon Macie

Amazon SageMaker Data Wrangler

NEW QUESTION 48
A car sales company maintains data about cars that are listed for sale in an area. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to- date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?

Use an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.

Use a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

Use AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows.
Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.

Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one- time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

NEW QUESTION 49
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?

Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.

Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.

Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.

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
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket.
The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment.
Which solution will package and deploy this serverless data pipeline?

Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command to prepare the deployment package. Run the sam deploy –guided command to deploy the pipeline.

Run the sam deploy command directly with the –s3-bucket parameter to deploy the Lambda function code. Manually configure the S3 event trigger in the AWS Management Console.

Add the first S3 bucket to the SAM template. Run the sam package template to upload the Lambda function code to Amazon S3. Create an AWS CloudFormation stack from the packaged template. Configure event notifications manually.

Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command followed by the aws cloudformation deploy command to deploy the pipeline.

NEW QUESTION 52
A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)

Ensure that the object format, compression type, and schema are the same for each object.

Ensure that the object format and schema are the same for each object. Do not enforce consistency for the compression type of each object.

Ensure that the schema is the same for each object. Do not enforce consistency for the file format and compression type of each object.

Ensure that the structure of the prefix for each S3 object name is consistent.

Ensure that all S3 object names follow a similar pattern.

NEW QUESTION 53
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?

Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.

Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

NEW QUESTION 54
A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?

Use both server-side encryption with AWS KMS keys (SSE-KMS) and the Amazon S3 Encryption Client.

Use dual-layer server-side encryption with AWS KMS keys (DSSE-KMS).

Use server-side encryption with customer-provided keys (SSE-C) before files are uploaded.

Use server-side encryption with AWS KMS keys (SSE-KMS).

NEW QUESTION 55
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams’ access to the datashare.
Which command or commands will meet this requirement?

GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS ‘<account ID 1>’ AND ‘<account ID 2>’;

GRANT USAGE ON DATASHARE salesshare TO NAMESPACES ‘<account ID 1>’ AND
‘<account ID 2>’;

GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘<account ID 1>’;
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘<account ID 2>’;

GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘<account ID 1>’;GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘<account ID 2>’;

NEW QUESTION 56
A data engineer created a table named cloudtrail_logs in Amazon Athena to query AWS CloudTrail logs and prepare data for audits. The data engineer needs to write a query to display errors with error codes that have occurred since the beginning of 2024. The query must return the
10 most recent errors.
Which query will meet these requirements?

select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere errorcode is not nulland eventtime >= ‘2024-01-01T00:00:00Z’ group by eventname, errorcode, errormessageorder by TotalEvents desclimit 10;

select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where eventtime >= ‘2024-01-01T00:00:00Z’ group by eventname, errorcode, errormessage order by TotalEvents desc limit 10;

select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere eventtime >= ‘2024-01-01T00:00:00Z’ group by eventname, errorcode, errormessageorder by eventname asc limit 10;

select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where errorcode is not nulland eventtime >= ‘2024-01-01T00:00:00Z’ group by eventname, errorcode, errormessagelimit 10;

NEW QUESTION 57
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.

Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

NEW QUESTION 58
A data engineer must maintain and monitor a data pipeline on AWS that processes streaming data from Internet of Things (IoT) devices. The pipeline uses Amazon Kinesis Data Streams to ingest data and Amazon Data Firehose to deliver data to an Amazon S3 bucket. The data engineer needs to monitor the health of the pipeline. Which solution will meet these requirements with the LEAST operational effort?

Use Amazon CloudWatch Logs to manually review logs that are generated by Kinesis Data Streams and Firehose.

Configure Amazon CloudWatch alarms to monitor key metrics such as IncomingBytes, OutgoingBytes, and DeliveryToS3.Success for Kinesis Data Streams and Firehose

Use an AWS Lambda function to run daily checks on the status of the Kinesis Data Streams and Firehose. Configure the Lambda function to use Amazon Simple Notification Service (Amazon SNS) to send notifications.

Use Amazon Managed Service for Apache Flink to perform near real-time anomaly detection on the streaming data and to invoke alerts if unusual patterns are detected.

NEW QUESTION 59
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?

SELECT FROM SYSTEM$PIPE_STATUS (”test.’extract’.my_pipe”i:

SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I

SELE2T * FROM SYSTEM$PIPE_STATUS < ‘ test. “Extract”, my_pipe’);

SELECT * FROM SYSTEM$PIPE_STATUS (“test. ‘extract’ .my_pipe”};

NEW QUESTION 60
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, “No space left on device.” The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Choose two.)

Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.

Enable the –write-shuffie-files-to-s3 job parameter. Use the salting technique.

Use error logs in Amazon CloudWatch to monitor data skew.

Scale out the workers vertically to address data skewness.

Scale out the number of workers horizontally to address data skewness.

NEW QUESTION 61
A company generates reports from 30 tables in an Amazon Redshift data warehouse. The data source is an operational Amazon Aurora MySQL database that contains 100 tables. Currently, the company refreshes all data from Aurora to Amazon Redshift every hour, which causes delays in report generation. Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)

Use AWS Database Migration Service (AWS DMS) to create a replication task. Select only the required tables.

Create a database in Amazon Redshift that uses the integration.

Create a zero-ETL integration in Amazon Aurora. Select only the required tables.

Use query editor v2 in Amazon Redshift to access the data in Aurora.

Create an AWS Glue job to transfer each required table. Run an AWS Glue workflow to initiate the jobs every 5 minutes.

NEW QUESTION 62
Streams record the differences between two offsets. If a row is added and then updated in the cur-rent offset, what will be the value of METADATA$ISUPDATE Columns in this scenario?

TRUE

FALSE

UPDATE

INSERT

NEW QUESTION 63
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?

YES

NEW QUESTION 64
For enabling non-ACCOUNTADMIN Roles to Perform Data Sharing Tasks, which two glob-al/account privileges snowflake provide?

CREATE SHARE

IMPORT SHARE

REFERENCE USAGE

OPERATE

NEW QUESTION 65
Which use case would be BEST suited for the search optimization service?

Analysts who need to perform aggregates over high cardinality columns

Business users who need fast response times using highly selective filters

Data Scientists who seek specific JOIN statements with large volumes of data

Data Engineers who create clustered tables with frequent reads against clustering keys

NEW QUESTION 66
A company needs to build a data pipeline to process a 1-TB file from an Amazon S3 bucket. The pipeline needs to create three DataFrames based on business logic. The pipeline must save all three DataFrames to a second S3 bucket in parallel. The company needs to set the pipeline to be the target of an Amazon EventBridge rule that matches file uploads to the source S3 bucket.
Which solution will meet these requirements with the LEAST maintenance overhead?

Configure an Apache Spark Streaming application on Amazon EMR to process data from the S3 source bucket in batches, create DataFrames, and save the output to the destination S3 bucket.

Configure three AWS Lambda functions to process the business logic and to save the DataFrames to the destination S3 bucket in parallel.

Configure an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.

Configure an AWS Step Functions state machine to initiate an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.

NEW QUESTION 67
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse.
The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

Use the SUPER data type to store the data in the Amazon Redshift table.

Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.

Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

NEW QUESTION 68
A table is loaded using Snowpipe and truncated afterwards Later, a Data Engineer finds that the table needs to be reloaded but the metadata of the pipe will not allow the same files to be loaded again.
How can this issue be solved using the LEAST amount of operational overhead?

Wait until the metadata expires and then reload the file using Snowpipe

Modify the file by adding a blank row to the bottom and re-stage the file

Set the FORCE=TRUE option in the Snowpipe COPY INTO command

Recreate the pipe by using the create or replace pipe command

NEW QUESTION 69
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?

Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.

Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.

Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.

Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.

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[Q47-Q69] Use Real DEA-C01 – 100% Cover Real Exam Questions [Sep-2026]

Snowflake DEA-C01 Exam Syllabus Topics:

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