Updated PDF (New 2026) Actual Snowflake DEA-C01 Exam Questions
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NEW QUESTION # 43
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu- east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
- A. Set up an AWS DMS replication instance in a new AWS account in eu-west-1.
- B. Set up an AWS DMS replication instance in Account_B in eu-west-1.
- C. Set up an AWS DMS replication instance in Account_B in eu-east-1.
- D. Set up an AWS DMS replication instance in Account_A in eu-east-1.
Answer: C
Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.Redshift.html#CHAP_Target.R edshift.Prerequisites
NEW QUESTION # 44
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?
- A. TRUE
(Correct) - B. FALSE
Answer: A
NEW QUESTION # 45
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?
- A. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
- B. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
- C. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
- D. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
Answer: B
Explanation:
Data Quality Ruleset: Creating a ruleset with Data Quality Definition Language (DQDL) rules allows for defining and evaluating data quality on specific AWS Glue tables, enabling automated checks on data quality.
Scheduled Execution: Running the ruleset daily ensures that data quality scores are regularly updated.
AWS Glue Data Source in Amazon DataZone: Configuring Amazon DataZone with an AWS Glue data source enables seamless integration, allowing data quality scores from AWS Glue Data Quality to be published to the Amazon DataZone portal.
NEW QUESTION # 46
A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket.
A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Set up an Amazon EventBridge event that initiates the AWS Glue workflow after every successful S3 File Gateway file transfer event.
- B. Set up an AWS Lambda function that will invoke the AWS Glue Workflow. Set up an event for the creation of an S3 object as a trigger for the Lambda function.
- C. Set up an on-demand AWS Glue workflow so that the data engineer can start the AWS Glue workflow when each file transfer is complete.
- D. Determine when the file transfers usually finish based on previous successful file transfers. Set up an Amazon EventBridge scheduled event to initiate the AWS Glue jobs at that time of day.
Answer: A
Explanation:
Using EventBridge directly to trigger the AWS Glue workflow upon S3 events is straightforward and leverages AWS's event-driven architecture, requiring minimal maintenance.
NEW QUESTION # 47
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?
- A. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
- B. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).
- C. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
- D. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = false).
Answer: D
Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is PAUSED.
3. Recreate the pipe (using CREATE OR REPLACE PIPE).
4. Pause the pipe again.
5. Review the configuration steps for your cloud messaging service to ensure the settings are still accurate.
6. Query the SYSTEM$PIPE_STATUS function again and verify that the pipe execution state is RUNNING.
NEW QUESTION # 48
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?
- A. Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
- B. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges.
Use AWS Glue Data Catalog tables to check timestamp completeness. - C. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values.
- D. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.
Answer: C
Explanation:
AWS Glue DataBrew provides a low-code way to implement automated data quality checks, including completeness rules to flag missing timestamp values and numeric range validation to flag temperature readings outside -50°C to 150°C.
NEW QUESTION # 49
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.
NEW QUESTION # 50
Data Engineer is looking out to delete staged files automatically/periodically when the data is suc-cessfully loaded into tables by the Snowpipe. For achieving the same, which options/command is best suited: [Select 2]
- A. To remove staged files that no longer needed, periodically DELETE command can be executed to delete the files.
- B. To remove staged files that no longer needed, periodically REMOVE command can be executed to delete the files.
- C. PURGE option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
- D. REMOVE_STAGE_FILES option can be set as True in the COPY INTO Command embedded in PIPE objects definition.
Answer: B,C
Explanation:
Explanation
Deleting Staged Files After Snowpipe Loads the Data
Pipe objects do not support the PURGE copy option. Snowpipe cannot delete staged files automat-ically when the data is successfully loaded into tables.
To remove staged files that you no longer need, It is recommended to periodically executing the REMOVE command to delete the files.
Alternatively, configure any lifecycle management features provided by cloud storage service pro-vider.
NEW QUESTION # 51
A secure function returns data coming through an inbound share
What will happen if a Data Engineer tries to assign usage privileges on this function to an outbound share?
- A. An error will be returned because the Engineer cannot share data that has already been shared
- B. An error will be returned because only secure functions can be shared with inbound shares
- C. An error will be returned because only views and secure stored procedures can be shared
- D. The Engineer will be able to share the secure function with other accounts
Answer: A
Explanation:
Explanation
An error will be returned because the Engineer cannot share data that has already been shared. A secure function is a Snowflake function that can access data from an inbound share, which is a share that is created by another account and consumed by the current account. A secure function can only be shared with an inbound share, not an outbound share, which is a share that is created by the current account and shared with other accounts. This is to prevent data leakage or unauthorized access to the data from the inbound share.
NEW QUESTION # 52
Select the incorrect statements regarding Clustering depth?
- A. It helps Monitoring the clustering "health" of a large table, particularly over time as DML is performed on the table.
- B. The clustering depth for a populated table measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. The smaller the aver-age depth, the better clustered the table is with regards to the specified columns.
- C. Clustering depth can be used for determining whether a large table would benefit from explicitly defining a clustering key.
- D. A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 1.
(Correct)
Answer: D
Explanation:
Explanation
A table with no micro-partitions (i.e. an unpopulated/empty table) has a clustering depth of 0.
NEW QUESTION # 53
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the LEAST operational overhead?
- A. Store credentials in an encrypted Amazon S3 bucket. Rotate the credentials every month by using an S3 Lifecycle policy. Use bucket policies to control access.
- B. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
- C. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
- D. Use AWS Key Management Service (AWS KMS) to generate encryption keys. Configure automatic key rotation. Store the encrypted credentials in an Amazon DynamoDB table.
Answer: B
Explanation:
AWS Secrets Manager is purpose-built to store sensitive credentials and rotate them automatically without requiring the application code to be updated for each password change.
The application retrieves the current secret dynamically through API calls, so credential rotation happens transparently while minimizing operational overhead.
NEW QUESTION # 54
A data engineering team is using an Amazon Redshift data warehouse for operational reporting.
The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?
- A. STL_QUERY_METRICS
- B. STL_USAGE_CONTROL
- C. STL_ALERT_EVENT_LOG
- D. STL_PLAN_INFO
Answer: C
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm_chap_system-tables.html STL_ALERT_EVENT_LOG table view to meet this requirement. This system table in Amazon Redshift is designed to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
NEW QUESTION # 55
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
- A. Use a metastore on an Amazon RDS for MySQL DB instance.
- B. Use the AWS Glue Data Catalog.
- C. Use Amazon EMR and Apache Ranger.
- D. Use a Hive metastore on an EMR cluster.
Answer: B
Explanation:
https://aws.amazon.com/blogs/big-data/metadata-classification-lineage-and-discovery-using- apache-atlas-on-amazon-emr/
NEW QUESTION # 56
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database.
The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?
- A. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
- B. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
- C. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
- D. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
Answer: D
Explanation:
AWS DMS with ongoing replication (CDC) continuously streams new RDS for MySQL transactions into Amazon Redshift, providing near real-time freshness with minimal maintenance and transferring only changes instead of full batches. Reports can then run in Redshift joining current and historical data without hourly ETL jobs or S3 staging.
NEW QUESTION # 57
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?
- A. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
- B. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
- C. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
- D. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
- E. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
Answer: A
Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.
NEW QUESTION # 58
A company has as JSON file that contains personally identifiable information (PII) data and non- PII data. The company needs to make the data available for querying and analysis.
The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation.
Use Lake Formation permissions to provide access to the required data based on the type of user. - B. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.
- C. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.
- D. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.
Answer: A
Explanation:
AWS Lake Formation lets you register your JSON file as a table in the data catalog and apply fine-grained permissions (down to columns or rows) without moving or transforming the underlying data. You can grant all employees access to the non-PII columns while restricting the PII columns to a small group, achieving your security requirements with minimal operational overhead.
NEW QUESTION # 59
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