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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 129
Which of the below concepts/functions helps while implementing advanced Column-level Security?
- A. INVOKER_ROLE
- B. CURRENT_ROLE
- C. Role Hierarchy
- D. CURRENT_CLIENT
Answer: A,B,C
Explanation:
Explanation
Column-level Security supports using Context Functions in the conditions of the masking policy body to enforce whether a user has authorization to see data. To determine whether a user can see data in a given SQL statement, it is helpful to consider:
Masking policy conditions using CURRENT_ROLE target the role in use for the current session.
Masking policy conditions using INVOKER_ROLE target the executing role in a SQL statement.
Role hierarchy
Determine if a specified role in a masking policy condition (e.g. ANALYST custom role) is a lower privilege role in the CURRENT_ROLE or INVOKER_ROLE role hierarchy. If so, then the role returned by the CURRENT_ROLE or INVOKER_ROLE functions inherits the privileges of the specified role.
NEW QUESTION # 130
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?
- A. Wait state
- B. Parallel state
- C. Map state
- D. Choice state
Answer: C
Explanation:
Map state is designed precisely for the requirement described. It allows you to iterate over a collection of items, processing each item individually. The Map state can automatically manage the iteration and execute the specified transformation on each item in parallel, making it the perfect choice for parallel processing of a large collection of data files.
NEW QUESTION # 131
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters. Which solution will meet these requirements?
- A. Convert the dataset to JSON format before running Athena queries.
- B. Use Amazon Redshift Spectrum to query the data in Amazon S3.
- C. Use Amazon EMR to pre-process the data before running Athena queries.
- D. Configure query result reuse settings in the Athena workgroup.
Answer: D
Explanation:
Athena query result reuse allows repeated queries with the same text and parameters to return previously computed results instead of scanning the source data again. This improves performance for repeated queries and reduces query costs by avoiding unnecessary data scans.
NEW QUESTION # 132
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account B to process the data from the data stream. The company creates a Lambda execution role in Account B that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?
- A. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.
- B. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
- C. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.
- D. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account A.
Answer: C
Explanation:
To enable cross-account Lambda processing of Kinesis Data Streams, the stream in Account A must explicitly allow the Lambda execution role from Account B. This is done by adding a resource-based policy on the Kinesis data stream to grant kinesis:SubscribeToShard and related read permissions to the cross-account role. Without this resource-based policy, the Lambda in Account B cannot consume the data.
NEW QUESTION # 133
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?
- A. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
- B. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
- C. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
- D. AWS DMS only supports full-load migrations, which would require downtime for the source database.
- E. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.
Answer: E
NEW QUESTION # 134
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:
- A. VALIDATION_MODE instructs the COPY command to validate the data files instead of loading them into the specified table; i.e., the COPY command tests the files for er-rors but does not load them.
- B. VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error.
- C. VALIDATION_MODE option supported these values:
RETURN_n_ROWS,
RETURN_ERRORS,
RETURN_ALL_ERRORS - D. VALIDATION_MODE only support Data loading operation i.e., do not work while da-ta unloading.
Answer: A,B,C
Explanation:
Explanation
All the Statements are correct except the statement saying VALIDATION_MODE only support Data loading operation.
VALIDATION_MODE can be used with COPY INTO <location> command as well i.e for data unloading operation.
VALIDATION_MODE = RETURN_ROWS can be used at the time of Data unloading.
This option instructs the COPY command to return the results of the query in the SQL statement instead of unloading the results to the specified cloud storage location. The only supported valida-tion option is RETURN_ROWS. This option returns all rows produced by the query.
When you have validated the query, you can remove the VALIDATION_MODE to perform the unload operation.
NEW QUESTION # 135
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. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
- B. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
- C. 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.
- 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: A
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 # 136
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?
- A. SQL
- B. JAVA
- C. JAVASCRIPT
- D. PYTHON
Answer: C
NEW QUESTION # 137
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket.
An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
- A. Train and use the AWS Glue PySpark Filter class in the ETL job.
- B. Train and use the AWS Lake Formation FindMatches transform in the ETL job.
- C. Use Amazon Macie pattern matching as part of the ETL job.
- D. Partition tables and use the ETL job to partition the data on a unique identifier.
Answer: B
Explanation:
AWS Lake Formation provides machine learning capabilities to create custom transforms to cleanse your data. There is currently one available transform named FindMatches. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This will not require writing any code or knowing how machine learning works.
NEW QUESTION # 138
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?
- A. OPEARTE
- B. MANAGE GRANTS
- C. MODIFY
- D. OWNERSHIP
Answer: D
NEW QUESTION # 139
A finance company uses Amazon Redshift as a data warehouse. The company stores the data in a shared Amazon S3 bucket. The company uses Amazon Redshift Spectrum to access the data that is stored in the S3 bucket. The data comes from certified third-party data providers. Each third-party data provider has unique connection details.
To comply with regulations, the company must ensure that none of the data is accessible from outside the company's AWS environment.
Which combination of steps should the company take to meet these requirements? (Choose two.)
- A. Define table constraints for the primary keys and the foreign keys.
- B. Use federated queries to access the data from each data provider. Do not upload the data to the S3 bucket. Perform the federated queries through a gateway VPC endpoint.
- C. Create an AWS CloudHSM hardware security module (HSM) for each data provider. Encrypt each data provider's data by using the corresponding HSM for each data provider.
- D. Turn on enhanced VPC routing for the Amazon Redshift cluster. Set up an AWS Direct Connect connection and configure a connection between each data provider and the finance company's VPC.
- E. Replace the existing Redshift cluster with a new Redshift cluster that is in a private subnet. Use an interface VPC endpoint to connect to the Redshift cluster. Use a NAT gateway to give Redshift access to the S3 bucket.
Answer: D,E
NEW QUESTION # 140
A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector.
Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long How can this situation be improved?
- A. Define a clustering key for the sales data table
- B. Increase the number of maximum clusters of the virtual warehouse
- C. Increase the size of the virtual warehouse
- D. Rewrite the report to eliminate the use of the loop construct
Answer: D
Explanation:
Explanation
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake's parallel processing and optimization capabilities and reduce the network overhead and latency.
NEW QUESTION # 141
A company is designing a data lake on Amazon S3. To ensure high performance when accessing the data, which best practice should the company adopt in organizing its data in the S3 bucket?
- A. Use a flat structure by avoiding the creation of any prefix or "folder" hierarchy.
- B. Partition data based on commonly accessed attributes and use a consistent naming scheme for prefixes.
- C. Store all data files as a single large file and use AWS Lambda to parse required data segments.
- D. Enable S3 Transfer Acceleration to ensure data is quickly accessible from any location.
Answer: B
NEW QUESTION # 142
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?
- A. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1
- B. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.1
- C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
- D. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
Answer: C
Explanation:
AWS Glue Data Quality uses the ColumnValues rule with pattern matching to validate that values conform to a required format, such as an email pattern. Setting the threshold to greater than 0.9 ensures that at least 90% of the values in the Email column match the expected email format, causing the rule to fail if data quality drops below this level.
NEW QUESTION # 143
A gaming company uses a NoSQL database to store customer information. The company is planning to migrate to AWS.
The company needs a fully managed AWS solution that will handle high online transaction processing (OLTP) workload, provide single-digit millisecond performance, and provide high availability around the world.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Amazon Timestream
- B. Amazon DynamoDB
- C. Amazon Keyspaces (for Apache Cassandra)
- D. Amazon DocumentDB (with MongoDB compatibility)
Answer: B
NEW QUESTION # 144
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data.
The company needs to implement data quality validations. The validations must include rules for known issues. The validations must have the ability to automatically detect unexpected data quality issues.
Which solution will meet these requirements with the LEAST operation overhead?
- A. Use AWS Glue jobs to implement data quality validations that use SQL statements.
- B. Use AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly detection.
- C. Use AWS Glue DataBrew to profile the data. Configure data quality rules based on the data quality results from the profiling.
- D. Use AWS Glue jobs to implement data quality rules that use open source data quality frameworks.
Answer: B
Explanation:
AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.
NEW QUESTION # 145
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks.
The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student's selected language. Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.
- B. Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.
- C. Create a knowledge base in Amazon Bedrock Knowledge Bases with the university's textbooks.
Configure a multilingual model to generate responses with source citations. - D. Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university's textbooks to generate personalized feedback with citations.
Answer: C
Explanation:
Amazon Bedrock Knowledge Bases is the lowest-overhead solution because it provides a managed retrieval-augmented generation workflow over the university's textbooks and can return generated responses with citations to the source material. The RetrieveAndGenerate capability is designed to generate answers from retrieved textbook content and include source citations, which directly matches the requirement for accurate references to specific course materials. By choosing a multilingual model in Bedrock for generation, the application can respond in the student's selected language without building and operating a custom retrieval stack.
Reference:
https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-retrieve-generate.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-how-retrieval.html
NEW QUESTION # 146
Dominic, a Data Engineer wants to resume the pipe named stalepipe3 which got stale after 14 days. To do the same, he called the SYSTEM$PIPE_FORCE_RESUME function select sys-tem$pipe_force_resume('snowmydb.mysnowschema.stalepipe3','staleness_check_override'); Let's say If the pipe is resumed 16 days after it was paused, what will happened to the event notifi-cation that were received on the first and second days after the pipe was paused?
- A. Pipe maintains Metadata history of files for 64 days, so in this scenarios Snowpipe pro-cessed all the event notifications that were received for 16 days or so.
- B. All the events get processed from day 1 if the PURGE properties in the PIPE object definition set to be FALSE initially.
- C. Once the Pipe got stale, all the events got purged automatically & pipe needs to be rec-reated with modified properties.
- D. Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused.
Answer: D
Explanation:
Explanation
When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
To resume a stale pipe, a qualified role must call the SYSTEM$PIPE_FORCE_RESUME function and input the STALENESS_CHECK_OVERRIDE argument. This argument indicates an under-standing that the role is resuming a stale pipe.
For example, resume the stale stalepipe1 pipe in the mydb.myschema database and schema:
select sys-tem$pipe_force_resume('mydb.myschema.stalepipe3','staleness_check_override'); As an event notification received while a pipe is paused reaches the end of the limited retention pe-riod, Snowflake schedules it to be dropped from the internal metadata. If the pipe is later resumed, Snowpipe processes these older notifications on a best effort basis. Snowflake cannot guarantee that they are processed.
For example, if a pipe is resumed 15 days after it was paused, Snowpipe generally skips any event notifications that were received on the first day the pipe was paused (i.e. that are now more than 14 days old).
If the pipe is resumed 16 days after it was paused, Snowpipe generally skips any event notifications that were received on the first and second days after the pipe was paused. And so on.
NEW QUESTION # 147
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between
100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
- B. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
- C. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command.
Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger. - D. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
Answer: C
Explanation:
Configuring your S3 bucket to emit "Object Created" events into EventBridge and using a rule to invoke a lightweight Lambda function lets you automatically run a Redshift COPY for each new CSV. This serverless, event-driven pattern requires no continuously running infrastructure and has minimal operational overhead.
NEW QUESTION # 148
By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 149
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