Latest DEA-C01 Actual Free Exam Updated 387 Questions [Q84-Q108]

Latest DEA-C01 Actual Free Exam Updated 387 Questions [Q84-Q108]

August 31, 2026 latestexam 0 Comments

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Latest DEA-C01 Actual Free Exam Updated 387 Questions

Online Questions – Valid Practice DEA-C01 Exam Dumps Test Questions

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	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 3	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 4	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 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.

NO.84 If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

Snowflake automatically handles leading spaces by trimming implicitly & removes the quotation marks enclosing each field.

field_optionally_enclosed_by option along with TRIM_IF function in COPY INTO statement can be used to handle this scenario successfully.

Snowflake reads the leading space rather than the opening quotation character as the beginning of the field and the quotation characters are interpreted as string data.
(Correct)

COPY command trims the leading space and removes the quotation marks enclosing each field
1.copy into SFtable
2.from @%SFtable
3.file_format = (type = csv trim_space=true field_optionally_enclosed_by = ‘0x22’);

NO.85 A company needs to store semi-structured transactional data for an application in a database.
The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?

Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.

Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.

Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.

Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.

NO.86 A company aggregates high-frequency sensor telemetry into an Amazon S3 data lake. Each sensor stream emits structured records every hour. The records include metadata such as sensor category, unit ID, operational state, event timestamp, and site location. The data scales up to millions of records each day. The company runs complex queries each day to uncover performance insights specific to sensor categories. Which solution will meet these requirements with the FASTEST query execution time?

Persist the data in Apache ORC format. Partition the data by date. Sort the data by sensor category.

Persist the data in CSV format. Partition the data by date. Sort the data by operational status.

Persist the data in Parquet format. Partition the data by sensor category. Sort the data by date

Persist the data in CSV format. Partition the data by date. Sort the data by sensor category.

NO.87 A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Create a daily schedule to run the crawler. Specify a database name for the output.

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler’s data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

NO.88 A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team’s BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.

Create materialized views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

Create database views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

NO.89 A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?

Use AWS Glue job bookmarks to track the data for accuracy and consistency.

Create custom AWS Glue Data Quality rulesets to define specific data quality checks.

Use the built-in AWS Glue Data Quality transforms for standard data quality validations.

Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.

NO.90 A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?

Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.

Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.

Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.

Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.

NO.91 A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?

Use Amazon Kinesis Data Firehose and an AWS Lambda function to transform the data and deliver the transformed data to OpenSearch Service.

Use a Logstash pipeline that has prebuilt filters to transform the data and deliver the transformed data to OpenSearch Service.

Use an AWS Lambda function to call the Amazon Kinesis Agent to transform the data and deliver the transformed data OpenSearch Service.

Use the Kinesis Client Library (KCL) to transform the data and deliver the transformed data to OpenSearch Service.

NO.92 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).

NO.93 A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer’s original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

Replace sum(sales_amount) with count(*) for the aggregation.

Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.

Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.

Remove the GROUP BY clause.

NO.94 A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.

Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.

Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.

Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

NO.95 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.

NO.96 A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company’s data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database.
Configure the Sqoop job to write the data to Amazon S3 in Parquet format.

Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.

Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.

Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.

NO.97 A data engineer wants to improve the performance of SQL queries in Amazon Athena that run against a sales data table.
The data engineer wants to understand the execution plan of a specific SQL statement. The data engineer also wants to see the computational cost of each operation in a SQL query.
Which statement does the data engineer need to run to meet these requirements?

EXPLAIN SELECT * FROM sales;

EXPLAIN ANALYZE FROM sales;

EXPLAIN ANALYZE SELECT * FROM sales;

EXPLAIN FROM sales;

NO.98 Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.

For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.

For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.

For Step 7, Results do include table changes committed by Transaction 1.

if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.

NO.99 An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns.
A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena.
Which solution will meet these requirements with the LEAST operational effort?

Create an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.

Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.

Create an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.

Use Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.

NO.100 A company is developing a log streaming pipeline that uses Amazon Data Firehose. The pipeline streams Amazon CloudWatch Logs data to an Amazon S3 bucket. The company’s analytics team needs to use the data in audits. The pipeline must deliver only the relevant logs to the S3 bucket in a compatible format for the team’s analysis. Which solution will meet these requirements and maintain reliable performance?

Set the S3 bucket rules to allow logs from only specific timestamp ranges. Create an AWS Lambda function that converts the log files to the desired format. Use an S3 trigger to invoke the Lambda function.

Create a subscription filter in the CloudWatch Logs log group that uses the Firehose delivery stream as the destination. Create an AWS Lambda function that converts the log files to the desired format. Configure Firehose to invoke the Lambda function.

Create a subscription filter in the CloudWatch Logs log group. Configure the filter to monitor the Firehose stream. Create an AWS Lambda function to convert the log files to the desired format.
Configure Firehose to invoke the Lambda function.

Tag the CloudWatch Logs log groups that the analytics team needs. Configure Firehose to ingest only the tagged log groups. Configure Firehose to write the output in the desired format.

NO.101 A data engineer must implement a data cataloging solution to track schema changes in an Amazon Redshift table.
Which solution will meet these requirements?

Schedule an AWS Glue crawler to run every day on the table by using the Java Database Connectivity (JDBC) driver. Configure the crawler to update an AWS Glue Data Catalog.

Use AWS DataSync to log the table metadata to an AWS Glue Data Catalog. Use an AWS Glue crawler to update the Data Catalog every day.

Use the AWS Schema Conversion Tool (AWS SCT) to log the table metadata to an Apache Hive metastore. Use Amazon EventBridge Scheduler to update the metastore every day.

Schedule an AWS Glue crawler to run every day on the table. Configure the crawler to update an Apache Hive metastore.

NO.102 A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than
100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort?
(Choose two.)

Configure the third-party application to create the files in a columnar format.

Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

Partition the order data in the S3 bucket based on order date.

Configure the third-party application to create the files in JSON format.

Load the JSON data into the Amazon Redshift table in a SUPER type column.

NO.103 An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?

Convert the CSV logs into multiple ORC files for better parallelism in Athena. Partition by date in Amazon S3. Use columnar pushdown filters.

Convert the CSV logs to JSON. Partition by date in Amazon S3. Use Athena with dynamic filtering to reduce data scans.

Convert the CSV logs to Avro. Partition by date in Amazon S3. Use Athena with projection-based partitioning.

Convert the CSV logs to a single Apache Parquet file for each day Partition the data by date in Amazon S3. Use Athena with predicate pushdown filters.

NO.104 A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company’s current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

Use Hadoop Distributed File System (HDFS) as a persistent data store.

Use Amazon S3 as a persistent data store.

Use x86-based instances for core nodes and task nodes.

Use Graviton instances for core nodes and task nodes.

Use Spot Instances for all primary nodes.

NO.105 An ecommerce company uses AWS Glue ETL to process and analyze orders. The company wants to build an extract, transform, and load (ETL) pipeline that processes placed, shipped, delivered, and canceled orders differently.
The company integrates the order processing system with Amazon EventBridge. The company configures EventBridge Scheduler rules for each order status to invoke different AWS Glue workflows. When the company examines Amazon CloudWatch metrics for the workflow, the company notices that the FailedInvocations metric shows a high value for canceled orders.
The company must determine the cause of the failed invocations.
Which solution will meet this requirement?

Configure a dead-letter queue in EventBridge Scheduler to store failed events. Analyze the failed order events.

Use the archive and replay features in EventBridge Scheduler to investigate the issue.

Change the retry policy in EventBridge Scheduler to reduce the value for maximum retries.

Change the retry policy in EventBridge Scheduler to increase the value for maximum age of event.

NO.106 A company needs to use an Aws Glue PySpark job to read specific data from an Amazon DynamoDB table. The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data. Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?

Use the AWS Glue DynamoDB ETL connector to read the DynamoDB table. Use the filter option to read the required partition key.

Perform a query on the DynamoDB table in the AWS Glue job by using only the sort key in the key condition expression. Load the data into a DynamicFrame.

Perform a scan on the DynamoDB table in the Aws Glue job. Put the data into a DynamicFrame.
Filter the DynamicFrame on the partition key.

Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.

NO.107 A global bank is using Amazon S3 to store all financial transactions for its customers. Data is stored in multiple S3 buckets. The Security Team wants to perform a one-time assessment to determine if any credit card data is stored in one of Amazon’s S3 buckets. The team will be using Amazon Macie to make this discovery.
How sensitive data can be discovered?

Perform sensitive data discovery jobs with custom data identifiers.

Perform automated sensitive data discovery with managed data identifiers.

Perform sensitive data discovery jobs with managed data identifiers.

Perform automated sensitive data discovery with custom data identifiers.

NO.108 A company has an on-premises PostgreSQL database that contains customer data. The company wants to migrate the customer data to an Amazon Redshift data warehouse. The company has established a VPN connection between the on-premises database and AWS.
The on-premises database is continuously updated. The company must ensure that the data in Amazon Redshift is updated as quickly as possible.
Which solution will meet these requirements?

Use the pg_dump utility to generate a backup of the PostgreSQL database. Use the AWS Schema Conversion Tool (AWS SCT) to upload the backup to Amazon Redshift. Set up a cron job to perform a backup. Upload the backup to Amazon Redshift every night.

Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to use the change data capture (CDC) feature.

Use the pg_dump utility to generate a backup of the PostgreSQL database. Upload the backup to an Amazon S3 bucket. Use the COPY command to import the data into Amazon Redshift.

Create an AWS Database Migration Service (AWS DMS) full-load task. Set Amazon Redshift as the target. Configure the task to perform a full load of the database to Amazon Redshift every night.

DEA-C01 Exam PDF [2026] Tests Free Updated Today with Correct 387 Questions: https://www.latestcram.com/DEA-C01-exam-cram-questions.html

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Latest DEA-C01 Actual Free Exam Updated 387 Questions [Q84-Q108]

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