Snowflake DEA-C01 Practice Verified Answers - Pass Your Exams For Sure! [2026]
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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 165
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
- B. Manually review the data for custom PII categories.
- C. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
- D. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
Answer: D
Explanation:
AWS Glue DataBrew allows users to create custom data quality rules, including rules for detecting non-standard PII categories. By implementing custom rules, the data engineer can automate the detection of PII (including the custom categories) across multiple datasets without needing to manually review or develop custom scripts. This provides a solution with the least operational overhead, as it leverages the existing DataBrew framework and automation capabilities.
Manually reviewing the data is highly labor-intensive, error-prone, and not scalable, especially across multiple datasets.
While possible, writing custom Python scripts adds operational overhead, as it requires developing, maintaining, and integrating scripts with DataBrew.
This increases complexity compared to implementing custom rules directly within DataBrew.
While regex can be useful, incorporating it directly into ETL processes adds operational complexity and doesn't leverage the capabilities of DataBrew, which is already designed for data cleaning and quality validation.
NEW QUESTION # 166
A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?
- A. JSON format compressed with bzip2
- B. Apache Parquet format compressed with Snappy
- C. Apache Avro format compressed with LZO
- D. .csv format compressed with zip
Answer: B
Explanation:
Parquet provides efficient columnar storage, enabling Athena to read only the necessary data for queries, which reduces scan times and speeds up query performance.
Snappy compression offers a good balance between compression speed and efficiency, reducing storage costs without significantly impacting query times.
NEW QUESTION # 167
Data Engineer looking out for quick tool for understanding the mechanics of queries & need to know more about the performance or behaviour of a particular query.
He should go to which feature of snowflake which can help him to spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities?
- A. Query Profile
- B. Performance Metadata table
- C. Query Optimizer
- D. Query Designer
Answer: A
Explanation:
Explanation
Query Profile, available through the classic web interface, provides execution details for a query. For the selected query, it provides a graphical representation of the main components of the pro-cessing plan for the query, with statistics for each component, along with details and statistics for the overall query.
Query Profile is a powerful tool for understanding the mechanics of queries. It can be used whenev-er you want or need to know more about the performance or behavior of a particular query. It is de-signed to help you spot typical mistakes in SQL query expressions to identify potential performance bottlenecks and improvement opportunities.
NEW QUESTION # 168
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)
- A. Create a key named --conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.
- B. Set Iceberg as a value for the --datalake-formats job parameter.
- C. Specify the path to a specific version of Iceberg by using the -extra-jars job parameter. Set Iceberg as a value for the datalake-formats job parameter.
- D. Add the --job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.
- E. Set the --enable-auto-scaling parameter to true.
Answer: B,C
Explanation:
To run AWS Glue Apache Spark jobs with the Apache Iceberg framework, you must:
Set --datalake-formats to iceberg so Glue knows to use the Iceberg format.
Specify the Iceberg library by using the --extra-jars parameter to include the appropriate Iceberg JAR files for Glue to access the Iceberg table format.
NEW QUESTION # 169
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:
What does the total_constant_partition_count value indicate about this table?
- A. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
- B. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created
- C. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
- D. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering
Answer: C
Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.
NEW QUESTION # 170
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)
- A. Use Avro format. Use AWS Glue Data Catalog to track schema changes.
- B. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.
- C. Use ORC format. Store schema definitions in separate files in Amazon S3.
- D. Use ORC format. Use AWS Glue Data Catalog to track schema changes.
- E. Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.
Answer: B,D
Explanation:
ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution.
Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown; Glue Data Catalog tracks schema changes.
NEW QUESTION # 171
A company needs to send customer call data from its on-premises PostgreSQL database to AWS to generate near real-time insights. The solution must capture and load updates from operational data stores that run in the PostgreSQL database. The data changes continuously.
A data engineer configures an AWS Database Migration Service (AWS DMS) ongoing replication task. The task reads changes in near real time from the PostgreSQL source database transaction logs for each table. The task then sends the data to an Amazon Redshift cluster for processing.
The data engineer discovers latency issues during the change data capture (CDC) of the task.
The data engineer thinks that the PostgreSQL source database is causing the high latency.
Which solution will confirm that the PostgreSQL database is the source of the high latency?
- A. Verify that logical replication of the source database is configured in the postgresql.conf configuration file.
- B. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCIncomingChanges metric to identify delays in the CDC from the source database.
- C. Enable Amazon CloudWatch Logs for the DMS endpoint of the source database. Check for error messages.
- D. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCLatencySource metric to identify delays in the CDC from the source database.
Answer: D
Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Troubleshooting_Latency.html A high CDCLatencySource metric indicates that the process of capturing changes from the source is delayed.
NEW QUESTION # 172
A data engineer is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket.
The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?
- A. Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.
- B. Use the Athena console to ensure the Athena queries also exclude the .json files.
- C. Use S3 bucket policies to block access to the .json files.
- D. Relocate the .json files to a different path within the S3 bucket.
Answer: D
Explanation:
Athena does not recognize exclude patterns that you specify an AWS Glue crawler. For example, if you have an Amazon S3 bucket that contains both .csv and .json files and you exclude the .json files from the crawler, Athena queries both groups of files. To avoid this, place the files that you want to exclude in a different location.
https://docs.aws.amazon.com/athena/latest/ug/troubleshooting-athena.html
NEW QUESTION # 173
A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode.
An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error.
Where can the data engineer find this error log?
- A. The engineer can connect to the persistent application UI to see the first YARN container log in the Spark UI.
- B. The engineer can connect to the Amazon EMR console to see the Amazon EMR step logs that are archived in Amazon S3.
- C. The engineer can connect to the web UI on the live cluster to see the YARN ResourceManager logs.
- D. The engineer can connect to the primary node of the cluster by using SSH to see the Spark history server logs.
Answer: B
Explanation:
In EMR cluster mode, the Spark driver runs inside the cluster and its standard error logs are captured as step logs. These logs are automatically archived to Amazon S3 and accessible from the Amazon EMR console under step logs. This is the correct location for investigating Spark driver errors.
NEW QUESTION # 174
Jonas, a Lead Performance Engineer,identifed that some of the operation of his query which func-tionally remove the duplicates from huge data set is spilling the data to remote disk. How can he alleviate spilling to a remote disk for better query performance?
- A. Jonas can recommend using a large warehouse which effectively increase the available memory/local disk space for the operations.
- B. Data Sharing can be helpful to improve query performance.
- C. Spilling do not have a profound effect on query performance (especially if remote disk is used for spilling).
- D. He can Process data in smaller batches to manage workload.
Answer: A,D
Explanation:
Explanation
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
To alleviate this, It is recommend that:
Using a larger warehouse (effectively increasing the available memory/local disk space for the op-eration), and/or Processing data in smaller batches.
NEW QUESTION # 175
Charles, A Lead Data engineer, with ACCOUNTADMIN role wants to configure the time travel for one of the Schema's object. He setup the MIN_DATA_RETENTION_TIME_IN_DAYS pa-rameter with Value 79 at account level but he figured out that DA-TA_RETENTION_TIME_IN_DAYS is already set with value 81 at account level. What would be the effective minimum data retention period for an object?
- A. There is no such MIN_DATA_RETENTION_TIME_IN_DAYS parameter
- B. 0
- C. 1
- D. 2
Answer: D
Explanation:
Explanation
A user with the ACCOUNTADMIN role can also set the MIN_DATA_RETENTION_TIME_IN_DAYS at the account level. This parameter setting enforc-es a minimum data retention period for databases, schemas, and tables. Setting MIN_DATA_RETENTION_TIME_IN_DAYS does not alter or replace the DA-TA_RETENTION_TIME_IN_DAYS parameter value. It may, however, change the effective data retention period for objects. When MIN_DATA_RETENTION_TIME_IN_DAYS is set at the ac-count level, the data retention period for an object is determined by MAX(DATA_RETENTION_TIME_IN_DAYS, MIN_DATA_RETENTION_TIME_IN_DAYS).
NEW QUESTION # 176
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead?
(Choose two.)
- A. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.
- B. Turn on the public access setting for the DB instance.
- C. Configure the Lambda function to run in the same subnet that the DB instance uses.
- D. Update the security group of the DB instance to allow only Lambda function invocations on the database port.
- E. Attach the same security group to the Lambda function and the DB instance. Include a self- referencing rule that allows access through the database port.
Answer: C,D
NEW QUESTION # 177
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use the Amazon Redshift Data API.
- B. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
- C. Establish WebSocket connections to Amazon Redshift.
- D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
Answer: A
Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html
NEW QUESTION # 178
Assuming a Data Engineer has all appropriate privileges and context which statements would be used to assess whether the User-Defined Function (UDF), MTBATA3ASZ. SALES .REVENUE_BY_REGION, exists and is secure? (Select TWO)
- A. SHOW EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION'IB SCHEMA SALES;
- B. SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;
- C. SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;
- D. SELECT IS_SEC"JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE
FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION'; - E. SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;
Answer: B,C
Explanation:
Explanation
The statements that would be used to assess whether the UDF, MTBATA3ASZ. SALES
.REVENUE_BY_REGION, exists and is secure are:
SHOW DS2R FUNCTIONS LIKE 'REVEX'^BYJIESION' IN SCHEMA SALES;: This statement will show information about the UDF, including its name, schema, database, arguments, return type, language, and security option. If the UDF does not exist, the statement will return an empty result set.
SELECT IS_SECURE FROM SNOWFLAKE. INFCRXATION_SCKZMA. FUNCTIONS WHERE
FUNCTI0N_3CHEMA = 'SALES' AND FUNCTI CN_NAXE = *ftEVEXUE_BY_RKXQH4;: This statement will query the SNOWFLAKE.INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current database. The statement will return the IS_SECURE column, which indicates whether the UDF is secure or not. If the UDF does not exist, the statement will return an empty result set. The other statements are not correct because:
SELECT IS_SEC"JRE FROM INFOR>LVTICN_SCHEMA. FUNCTIONS WHERE
FUNCTION_SCHEMA = 'SALES1 AND FUNGTZON_NAME = ' REVENUE_BY_REGION';: This statement will query the INFORMATION_SCHEMA.FUNCTIONS view, which contains metadata about the UDFs in the current schema. However, the statement has a typo in the schema name ('SALES1' instead of 'SALES'), which will cause it to fail or return incorrect results.
SHOW EXTERNAL FUNCTIONS LIKE 'REVENUE_BY_REGION' IB SCHEMA SALES;: This statement will show information about external functions, not UDFs. External functions are Snowflake functions that invoke external services via HTTPS requests and responses. The statement will not return any results for the UDF.
SHOW SECURE FUNCTIONS LIKE 'REVENUE 3Y REGION' IN SCHEMA SALES;: This
statement is invalid because there is no such thing as secure functions in Snowflake. Secure functions are a feature of some other databases, such as PostgreSQL, but not Snowflake. The statement will cause a syntax error.
NEW QUESTION # 179
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?
- A. EXPLAIN ANALYZE FROM sales;
- B. EXPLAIN ANALYZE SELECT * FROM sales;
- C. EXPLAIN SELECT * FROM sales;
- D. EXPLAIN FROM sales;
Answer: B
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/athena-explain-statement.html
NEW QUESTION # 180
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
- A. Referentialintegrity "city,state" "reference.{ref_city,ref_state}" = 100
- B. Referentiallntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0
- C. DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0
- D. DatasetMatch "reference" "city->ref_city, state->ref_state" = 100
Answer: B
Explanation:
The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those columns without extra overhead.
NEW QUESTION # 181
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account.
A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow.
Which log type should the data engineer use to diagnose the cause of the failure?
- A. YourEnvironmentName-Scheduler
- B. YourEnvironmentName-WebServer
- C. YourEnvironmentName-Task
- D. YourEnvironmentName-DAGProcessing
Answer: C
Explanation:
When a workflow fails to run in Amazon MWAA, the task logs (YourEnvironmentName-Task) are the most relevant for diagnosing the issue. Task logs contain detailed information about the execution of individual tasks within the workflow, including any error messages or stack traces that can help pinpoint the cause of the failure.
NEW QUESTION # 182
To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?
- A. OFFSET (time difference in seconds from the present time)
- B. AT | BEFORE clause which can be specified in the CREATE ... CLONE commands.
- C. ONSET (time difference in seconds from the present time)
- D. UNDROP command for tables, schemas, and databases.
- E. STATEMENT_ID (identifier for statement, e.g. query ID)
- F. STATEMENT (identifier for statement, e.g. query ID)
Answer: A,B,D,F
NEW QUESTION # 183
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?
- A. Amazon Macie
- B. Amazon SageMaker Data Wrangler
- C. Amazon S3 Storage Lens
- D. Amazon Textract
Answer: A
Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover and protect sensitive data, such as Personally Identifiable Information (PII), stored in Amazon S3. Macie can automatically scan the order summaries for PII, enabling the company to detect and then redact PII as required.
NEW QUESTION # 184
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?
- A. MANAGE GRANTS
- B. OWNERSHIP
- C. OPEARTE
- D. MODIFY
Answer: B
NEW QUESTION # 185
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
- A. session.sql()
- B. session.builder()
- C. DataFraas.writeO
- D. session.jdbc_connection()
- E. session,table()
- F. session.read.json{)
Answer: A,E,F
Explanation:
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.
NEW QUESTION # 186
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