DEA-C01 Dumps with Practice Exam Questions Answers
DEA-C01 by SnowPro Advanced Actual Free Exam Practice Test
Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 120
A marketing company uses Amazon S3 to store clickstream data. The company queries the data at the end of each day by using a SQL JOIN clause on S3 objects that are stored in separate buckets.
The company creates key performance indicators (KPIs) based on the objects. The company needs a serverless solution that will give users the ability to query data by partitioning the data.
The solution must maintain the atomicity, consistency, isolation, and durability (ACID) properties of the data.
Which solution will meet these requirements MOST cost-effectively?
- A. Amazon Redshift Spectrum
- B. Amazon EMR
- C. Amazon S3 Select
- D. Amazon Athena
Answer: D
NEW QUESTION # 121
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?
- A. Apply partition filters in the queries.
- B. Organize the data that is in Amazon S3 by using a nested directory structure.
- C. Increase the frequency of AWS Glue crawler invocations to update the data catalog more often.
- D. Configure Spark to use in-memory caching for frequently accessed data.
Answer: A
Explanation:
By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.
NEW QUESTION # 122
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?
- A. Switch to a multi-cluster virtual warehouse
- B. Rewrite the query to remove the CTEs.
- C. Increase the warehouse size
- D. Enable clustering on the table
Answer: C
Explanation:
Explanation
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.
NEW QUESTION # 123
A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years. Which solution will meet this requirement?
- A. Configure an S3 Lifecycle policy to move objects that are older than 7 years to S3 Glacier Deep Archive.
- B. Configure an S3 Lifecycle policy to permanently delete objects that are older than 7 years.
- C. Configure an S3 Lifecycle policy to enable S3 Object Lock on all objects that are older than 7 years.
- D. Use Amazon Athena to query the S3 bucket for objects that are older than 7 years. Configure Athena to delete the results.
Answer: B
Explanation:
An S3 Lifecycle policy can automatically and permanently expire (delete) objects after they reach an age of 7 years, ensuring the data is removed without ongoing manual processes.
NEW QUESTION # 124
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?
- A. Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
- B. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
- C. Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.
- D. Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
Answer: D
Explanation:
Two step approach involving creating a staging table, followed by using Redshift's merge statement to update the target table from staging table and finally truncate/housekeep the staging table.
NEW QUESTION # 125
A company wants to migrate an application and an on-premises Apache Kafka server to AWS.
The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?
- A. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster
- B. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless
- C. Amazon Kinesis Data Firehose
- D. Amazon Kinesis Data Streams
Answer: B
NEW QUESTION # 126
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: C
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/tutorial-add-crawler.html
NEW QUESTION # 127
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?
- A. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
- B. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
- C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
- D. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
Answer: B
Explanation:
Standard Step Functions support long-running, sequential workflows and the Wait for Callback pattern without charging for idle wait time, minimizing cost while ensuring completion within 24 hours. Express would bill for long waits, Lambda+EventBridge adds complexity and limits, and MWAA has higher operational overhead and cost.
NEW QUESTION # 128
A company uses Amazon EMR as an extract, transform, and load (ETL) pipeline to transform data that comes from multiple sources. A data engineer must orchestrate the pipeline to maximize performance.
Which AWS service will meet this requirement MOST cost effectively?
- A. Amazon EventBridge
- B. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- C. AWS Glue Workflows
- D. AWS Step Functions
Answer: D
Explanation:
Glue Workflows is for Glue job orchestration. C is for orchestration with different AWS services.
NEW QUESTION # 129
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?
- A. Use an AWS Glue custom crawler to read and transform the CSV files.
- B. Use AWS Glue DataBrew recipes to read and transform the CSV files.
- C. Use AWS Glue Python jobs to read and transform the CSV files.
- D. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
Answer: B
Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks.
While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew.
AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns.
Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.
NEW QUESTION # 130
A company receives call logs as Amazon S3 objects that contain sensitive customer information.
The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?
- A. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.
- B. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.
- C. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.
- D. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.
Answer: A
NEW QUESTION # 131
A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?
- A. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
- B. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
- C. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.
- D. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
Answer: D
Explanation:
https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on- amazon-emr/ Migrating the Hive metastore into Amazon EMR and using AWS Glue Data Catalog as an external catalog provides a balance between leveraging the scalable and managed services of AWS (like EMR and Glue Data Catalog) and ensuring a smooth transition from the on-premises setup. This approach leverages the serverless nature of AWS Glue Data Catalog, minimizing operational overhead and potentially reducing costs compared to managing database servers.
NEW QUESTION # 132
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 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.
- B. Snowflake supports resizing a warehouse at any time, even while running.
- C. 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.
- D. 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.
- E. 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).
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 # 133
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 SageMaker Data Wrangler
- B. Amazon Textract
- C. Amazon Macie
- D. Amazon S3 Storage Lens
Answer: C
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 # 134
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
- A. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.
- B. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
- C. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
- D. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
Answer: B
NEW QUESTION # 135
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 SELECT * FROM sales;
- B. EXPLAIN ANALYZE SELECT * FROM sales;
- C. EXPLAIN ANALYZE FROM sales;
- D. EXPLAIN FROM sales;
Answer: B
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/athena-explain-statement.html
NEW QUESTION # 136
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
- B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
- C. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
- D. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
Answer: C
Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/cbac-tutorial.html
NEW QUESTION # 137
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?
- A. figure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
- B. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.
- C. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.
- D. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
Answer: B
Explanation:
Amazon EventBridge can monitor the execution status of AWS Step Functions state machines and trigger specific actions based on state changes, such as when a state machine succeeds or fails. By configuring an EventBridge rule to capture these execution status changes and forward the details to an Amazon SNS topic, real-time notifications can be sent to alert users. This solution ensures a scalable and event-driven approach to monitoring the entire data processing pipeline in real time.
NEW QUESTION # 138
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 # 139
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. 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.
- B. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.
- 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
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 # 140
A company reads data from customer databases that run on Amazon RDS. The databases contain many inconsistent fields. For example, a customer record field that iPnamed place_id in one database is named location_id in another database. The company needs to link customer records across different databases, even when customer record fields do not match.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an AWS Glue crawler to craw the databases. Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data.
- B. Create a provisioned Amazon EMR cluster to process and analyze data in the databases.Connect to the Apache Zeppelin notebook. Use an Apache Spark ML model to find duplicate records in the data. Evaluate and tune the model by evaluating the performance and results.
- C. Create a provisioned Amazon EMR cluster to process and analyze data in the databases.
Connect to the Apache Zeppelin notebook. Use the FindMatches transform to find duplicate records in the data. - D. Create an AWS Glue crawler to craw the databases. Use the FindMatches transform to find duplicate records in the data. Evaluate and tune the transform by evaluating the performance and results.
Answer: D
NEW QUESTION # 141
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.
- A. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
- B. Python packages can only be loaded in a local environment
- C. Python packages can access any external endpoints
- D. Third-party supported Python packages are locked down to prevent hitting
- E. Querying information__schema .packages will provide a list of supported Python packages and versions
Answer: A,C,E
Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.
NEW QUESTION # 142
A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.
A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialItegrity rule for each join.
- B. Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.
- C. Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested column contain null values. Use a second SQL transform to check referential integrity.
- D. Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.
Answer: A
Explanation:
AWS Glue's built-in data quality evaluation transform lets you declaratively apply DQDL rules, like IsComplete for null checks and ReferentialIntegrity for joins, directly in your ETL job. This requires minimal custom code and no separate reporting or profiling infrastructure, giving you pre-load validations with the least operational overhead.
NEW QUESTION # 143
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile
- A. Data Warehouse
- B. Data Mart
- C. Data Hub
- D. Data Lake
Answer: D
NEW QUESTION # 144
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?
- A. Snowflake Schema
- B. Data Vault
- C. Data Hub
- D. Star Schema
Answer: B
Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.
NEW QUESTION # 145
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