[Jun 28, 2026] VCEDumps DEA-C01 Exam Practice Test Questions (Updated 312 Questions)
Pass Snowflake DEA-C01 Exam Info and Free Practice Test
Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 153
A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.
Which recommendations apply to this scenario? (Select THREE)
- A. Load large files (1 GB or larger)
- B. Verify each value of each unique element stores a single native data type (string or number)
- C. Ensure that data files are 100-250 MB (or larger) in size compressed
- D. Extract semi-structured data elements containing null values into relational columns before loading
- E. Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute
- F. Load a single huge array containing multiple records into a single table row
Answer: B,C,E
Explanation:
Explanation
The recommendations that apply to this scenario are:
Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smallerfiles can cause performance degradation or errors due to excessive metadata operations or network latency.
Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements:
string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.
Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.
NEW QUESTION # 154
Data Engineer try to load data from external stage using Snowpipe & later find out that some Set of Files Not Loaded. To debug the issue, she used COPY_HISTORY function & cross verified that its output indicates a subset of files was not loaded. What is possible reason of arising this situation in both REST API call and Auto-Ingest methods? [Select 2]
- A. An event notification failure prevented a set of files from getting queued.
- B. A backlog of data files already existed in the external stage do not have any impact on Load failure, as this is well managed by serverless SnowPipe
- C. Files modified and staged again after 14 days and Snowpipe ignores modified files that are staged again.
- D. External event-driven functionality is used to call the REST APIs, and a backlog of da-ta files already existed in the external stage before the events were configured.
Answer: A,D
Explanation:
Explanation
COPY_HISTORY Record Indicates Unloaded Subset of Files:
If the COPY_HISTORY function output indicates a subset of files was not loaded, you may try to "refresh" the pipe.
This situation can arise in any of the following situations:
The external stage was previously used to bulk load data using the COPY INTO table command.
REST API:
o External event-driven functionality is used to call the REST APIs, and a backlog of data files al-ready existed in the external stage before the events were configured.
Auto-ingest:
o A backlog of data files already existed in the external stage before event notifications were con-figured.
o An event notification failure prevented a set of files from getting queued.
To load the data files in your external stage using the configured pipe, execute an ALTER PIPE ... REFRESH statement.
NEW QUESTION # 155
Select the Incorrect statement about External Functions in SnowFlake?
- A. An external function is a type of UDF.
- B. Inside Snowflake, the external function is stored as a database object that contains in-formation that Snowflake uses to call the remote service.
- C. Inside Snowflake, the external function is stored as a API Integration object.
- D. An external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.
Answer: C
NEW QUESTION # 156
At what isolation level are Snowflake streams?
- A. Read committed
- B. Read uncommitted
- C. Snapshot
- D. Repeatable read
Answer: D
Explanation:
Explanation
The isolation level of Snowflake streams is repeatable read, which means that each transaction sees a consistent snapshot of data that does not change during its execution. Streams use time travel internally to provide this isolation level and ensure that queries on streams return consistent results regardless of concurrent transactions on their source tables.
NEW QUESTION # 157
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?
- A.
- B.
- C.
- D.
Answer: A
Explanation:
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.
NEW QUESTION # 158
A company stores server logs in an Amazon S3 bucket. The company needs to keep the logs for
1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Configure an AWS Step Functions state machine to delete the logs after 1 year.
- B. Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.
- C. Create an AWS Lambda function to delete the logs after 1 year.
- D. Define an S3 Lifecycle configuration to delete the logs after 1 year.
Answer: D
Explanation:
An S3 Lifecycle configuration is the best choice for this requirement because it allows the company to define a rule that will automatically delete objects in an S3 bucket after a specified period, in this case, 1 year. This solution provides the least operational overhead because it is a built-in feature of Amazon S3, requires no additional infrastructure or management, and is designed specifically for managing object lifecycles.
NEW QUESTION # 159
A company needs to optimize storage for an Amazon S3 bucket. Objects older than 1 year must be accessible within 5 hours. All versions of the objects must be retained and immutable for 7 years. All versions of the objects must use the write-once-read-many (WORM) model. Which solution will meet these requirements?
- A. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years. - B. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to deletes objects that are older than 7 years.
- C. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to delete objects that are older than 7 years. - D. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
Answer: A
Explanation:
S3 Object Lock enforces the write-once-read-many model and ensures that all object versions remain immutable for the required 7-year retention period. Transitioning objects older than 1 year to S3 Glacier Flexible Retrieval satisfies the requirement that data be accessible within 5 hours, as this storage class supports retrieval within minutes to a few hours. The lifecycle policy can expire objects after 7 years, aligning with the retention requirement once the Object Lock retention period has ended.
NEW QUESTION # 160
A Data Engineer is trying to load the following rows from a CSV file into a table in Snowflake with the following structure:
....engineer is using the following COPY INTO statement:
However, the following error is received.
Which file format option should be used to resolve the error and successfully load all the data into the table?
- A. FIELD OPTIONALLY ENCLOSED BY = " "
- B. ERROR_ON_COLUMN_COUKT_MISMATCH = FALSE
- C. ESC&PE_UNENGLO9ED_FIELD = '\\'
- D. FIELD_DELIMITER = ","
Answer: A
Explanation:
Explanation
The file format option that should be used to resolve the error and successfully load all the data into the table is FIELD_OPTIONALLY_ENCLOSED_BY = '"'. This option specifies that fields in the file may be enclosed by double quotes, which allows for fields that contain commas or newlines within them. For example, in row 3 of the file, there is a field that contains a comma within double quotes: "Smith Jr., John". Without specifying this option, Snowflake will treat this field as two separate fields and cause an error due to column count mismatch. By specifying this option, Snowflake will treat this field as one field and load it correctly into the table.
NEW QUESTION # 161
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:
The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?
- A. Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.
- B. Create a local secondary index (LSI) on DriverID.
- C. Create a filter expression that uses RiderID and RideStatus.
- D. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
Answer: A
Explanation:
To let drivers efficiently query only their completed rides, you need a global secondary index (GSI) with DriverID as the partition key (so queries can be targeted per driver) and RideStatus as the sort key (so you can query for "Completed" rides without scanning the full table). This avoids costly scans and supports fast, targeted lookups at scale.
NEW QUESTION # 162
Mohan, Data engineer works with ALTUSO Company, wants to programmatically check the status of the query. He needs query id to identify each query executed by Snowflake & using Snowflake Connector for Python to execute a query, how he will be able to meet this requirements. Select the best options you will suggest?
- A. He needs to query history views to get the queryID as best practices.
- B. He can access the query ID through the sfqid attribute in the Cursor object.
1.cur = con.cursor()
2.cur.execute("SELECT * FROM snowtable")
3.print(cur.sfqid) - C. Using python connector, snowflake does not support queryID retrieval for both syn-chronous & asynchronous query.
- D. When he used the Snowflake Connector for Python to execute a query, he can access the query ID through the pyqueryid attribute in the Cursor object.
Answer: B
Explanation:
Explanation
Retrieving the Snowflake Query ID
A query ID identifies each query executed by Snowflake. When you use the Snowflake Connector for Python to execute a query, you can access the query ID through the sfqid attribute in the Cursor object:
1.# Retrieving a Snowflake Query ID
2.cur = con.cursor()
3.cur.execute("SELECT * FROM testtable")
4.print(cur.sfqid)
NEW QUESTION # 163
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?
- A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks.
Load the processed data and the quality check results into a new S3 bucket. - B. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.
- C. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.
- D. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.
Answer: B
Explanation:
Using a single AWS Glue ETL job to both transform the data and invoke Glue Data Quality checks lets you declaratively enforce recommended rules without spinning up separate tools. You can then write the cleansed data and, if desired, the quality metrics, directly into your Amazon RDS for MySQL instance. This serverless, end-to-end approach minimizes service sprawl and only incurs Glue and RDS costs, making it the most cost-effective with the least operational overhead.
NEW QUESTION # 164
Snowpipe loads data from files as soon as they are available in a stage. Automated data loads lever-age event notifications for cloud storage to inform Snowpipe of the arrival of new data files to load. Which Cloud hosted platform provides cross cloud support for automated data loading via Snow-pipe?
- A. AZURE
- B. AWS
- C. GCP
- D. None of the Above currently provide cross cloud support for Snowpipe.
Answer: B
Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.
NEW QUESTION # 165
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]
- A. 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.
- B. 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.
- C. 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.
- D. 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).
- E. Snowflake supports resizing a warehouse at any time, even while running.
Answer: A,B,C,D,E
Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing 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. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
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.
NEW QUESTION # 166
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an Amazon EventBridge rule to match AWS Glue job failure events. Define an Amazon CloudWatch metric based on the EventBridge rule. Set up a CloudWatch alarm based on the metric to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
- B. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to target an AWS Lambda function that reads the logs and sends notifications to an Amazon Simple Notification Service (Amazon SNS) topic if AWS Glue job failure logs are present.
- C. Configure an Amazon CloudWatch Logs log group for the AWS Glue jobs. Create an Amazon EventBridge rule to match new log creation events in the log group. Configure the rule to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
- D. Create an Amazon EventBridge rule to match AWS Glue job failure events. Configure the rule to target an AWS Lambda function to process events. Configure the function to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.
Answer: A
Explanation:
By creating an EventBridge rule that matches Glue job state-change events where the state is FAILED, you can emit a custom CloudWatch metric without writing any code. Then you define a CloudWatch alarm on that metric to notify an SNS topic upon breach. This approach requires no Lambda functions or log parsing (just the rule, the metric, and the alarm) minimizing operational overhead.
NEW QUESTION # 167
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
- A. Use Amazon AppFlow to create a flow for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
- B. Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.
- C. Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.
- D. Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.
Answer: A
Explanation:
Amazon AppFlow provides fully managed, no-code connectors to a broad range of SaaS applications. By scheduling flows that land data in S3, you avoid building and maintaining custom API-calling code. Glue crawlers can catalog the files and make them available to QuickSight, giving you an end- to-end pipeline with minimal operational effort.
NEW QUESTION # 168
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
- B. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.
- C. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
- D. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.
Answer: A
Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.
NEW QUESTION # 169
When created, a stream logically takes an initial snapshot of every row in the source object and the contents of a stream change as DML statements execute on the source table.
A Data Engineer, Sophie Created a view that queries the table and returns the CURRENT_USER and CURRENT_TIMESTAMP values for the query transaction. A Stream has been created on views to capture CDC.
Tony, another user inserted the data e.g.
insert into <table> values (1),(2),(3);
Emily, another user also inserted the data e.g.
insert into <table> values (4),(5),(6);
What will happened when Different user queries the same stream after 1 hour?
- A. All the 6 records would be shown with METADATA$ACTION as 'INSERT' out of which 3 records would be displayed with username 'Tony' & rest 3 records would be displayed with username 'Emily'.
- B. User would be displayed with the one who queried during the session, but Recorded timestamp would be of past 1 hour i.e. actual records insertion time.
- C. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.
- D. All the Six Records would be displayed with CURRENT_USER & CUR-RENT_TIMESTAMP while querying Streams.
Answer: D
Explanation:
Explanation
When User queries the stream, the stream returns the username for the user. The stream also returns the current timestamp for the query transaction in each row, NOT the timestamp when each row was inserted.
NEW QUESTION # 170
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?
- A. AWS Glue workflows
- B. AWS Glue Studio
- C. AWS Step Functions
- D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
Answer: A
Explanation:
https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows- triggers-and-crawlers-with-custom-classifiers/
https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql- server-analysis-services-using-aws-glue/
NEW QUESTION # 171
......
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