Databricks Certified Data Engineer Associate Exam Dumps & Practice Test Questions
Question 1:
You are working with Delta Live Tables (DLT) in Databricks and have defined a dataset with a data quality constraint using the EXPECTATIONS clause. The clause includes the following condition:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW
When the system processes a batch containing records where the timestamp is earlier than '2020-01-01', causing a violation, what happens to the invalid records?
A. The records that violate the constraint are removed from the target dataset and placed into a quarantine table.
B. The invalid records remain in the target dataset, flagged as invalid in an additional field.
C. The records are dropped from the dataset, and their violation is logged in the event log.
D. The invalid records are included in the dataset but logged as violations in the event log.
E. The pipeline job fails due to the violation of the data quality constraint.
Answer: C
Explanation:
In Delta Live Tables (DLT) in Databricks, the EXPECTATIONS clause allows you to define data quality constraints that validate the data during processing. In this case, the constraint checks whether the timestamp is greater than '2020-01-01'. If the condition is violated (i.e., the timestamp is earlier than the specified date), the action specified in the ON VIOLATION clause determines how to handle these records.
What happens in this case?
The condition in the question specifies:
ON VIOLATION DROP ROW
This means that if a record violates the constraint (i.e., the timestamp is earlier than '2020-01-01'), the system will drop the violating row from the dataset.
In addition, violations are typically logged in the event log so that you can track the occurrence of data quality issues.
Why the other options are not correct:
A. The records that violate the constraint are removed from the target dataset and placed into a quarantine table:
While records that violate the constraint are dropped, they are not placed into a quarantine table. The DROP ROW action simply removes the row from the dataset, and it doesn't involve any quarantine or holding area.B. The invalid records remain in the target dataset, flagged as invalid in an additional field:
This is not true because the clause specifies DROP ROW. This action removes the row from the dataset entirely, so it is not flagged as invalid or retained in the dataset.D. The invalid records are included in the dataset but logged as violations in the event log:
Invalid records are not included in the dataset if they violate the constraint, as the action defined is to drop them. The violation may be logged in the event log, but the records themselves are not retained in the dataset.E. The pipeline job fails due to the violation of the data quality constraint:
There is no indication in the question that the pipeline would fail. The DROP ROW action simply removes the violating rows, and the job continues processing. The job does not fail unless explicitly specified in the pipeline configuration.
When a record violates the defined data quality constraint and DROP ROW is specified, the system removes the violating records from the dataset and logs the violation in the event log.
Thus, the correct answer is C.
Question 2:
In a Delta Live Tables (DLT) pipeline, you can create tables using either CREATE LIVE TABLE or CREATE STREAMING LIVE TABLE. The latter was previously known as CREATE INCREMENTAL LIVE TABLE.
If your pipeline processes data incrementally, when is it best to use the CREATE STREAMING LIVE TABLE syntax?
A. Use CREATE STREAMING LIVE TABLE when the subsequent steps of the pipeline use static processing.
B. Use CREATE STREAMING LIVE TABLE when processing the data incrementally.
C. CREATE STREAMING LIVE TABLE is not necessary and can be ignored in most cases.
D. Use CREATE STREAMING LIVE TABLE when your pipeline involves complex data aggregations.
E. Use CREATE STREAMING LIVE TABLE only when the previous step in the pipeline is static.
Answer: B
Explanation:
In Delta Live Tables (DLT), there are two ways to create tables based on the nature of your data processing: CREATE LIVE TABLE and CREATE STREAMING LIVE TABLE. The CREATE STREAMING LIVE TABLE syntax is specifically designed for cases where the pipeline processes data incrementally rather than in batch mode.
Why B is correct:
CREATE STREAMING LIVE TABLE is best used when you are processing data incrementally.
This means that your pipeline handles real-time or near real-time data, where only the new or changed records need to be processed. The syntax for streaming tables helps manage incremental updates to the data as new records arrive over time.
With CREATE STREAMING LIVE TABLE, Delta Live Tables will optimize the processing of newly arriving data, making it more efficient and ensuring that the data is continually processed in a streaming manner. This approach is especially useful for handling streaming data sources like Kafka or Kinesis, where you do not need to process the entire dataset but instead only process new or updated records.
Why the other options are not correct:
A. Use CREATE STREAMING LIVE TABLE when the subsequent steps of the pipeline use static processing:
If the subsequent steps of the pipeline involve static processing (i.e., processing that does not require incremental updates), there is no need to use CREATE STREAMING LIVE TABLE. Instead, you would use CREATE LIVE TABLE for processing static or batch data.C. CREATE STREAMING LIVE TABLE is not necessary and can be ignored in most cases:
This is incorrect because CREATE STREAMING LIVE TABLE is specifically needed when the pipeline processes data incrementally. Ignoring it would mean losing the benefits of handling streaming data efficiently.D. Use CREATE STREAMING LIVE TABLE when your pipeline involves complex data aggregations:
While complex data aggregations can be a part of a streaming pipeline, the use of CREATE STREAMING LIVE TABLE is based on how the data is processed (i.e., incrementally) rather than on the complexity of the aggregation itself. You would still use CREATE STREAMING LIVE TABLE when processing the data incrementally, even if the aggregations are complex.E. Use CREATE STREAMING LIVE TABLE only when the previous step in the pipeline is static:
This statement is incorrect. The CREATE STREAMING LIVE TABLE syntax is used when you want to process data incrementally, regardless of whether the previous step is static or not. If the previous step is static, CREATE LIVE TABLE should be used instead.
The CREATE STREAMING LIVE TABLE syntax is specifically designed for processing data incrementally, allowing the pipeline to handle real-time or near real-time data efficiently. This is the correct approach when dealing with streaming data sources and incremental processing.
Thus, the correct answer is B.
Question 3:
A data engineer is building a pipeline to process files generated by an external system and stored in a shared directory. The directory is accessed by multiple processes, meaning files cannot be deleted or modified by the pipeline. The pipeline should only process new files that are added after the last successful run.
Which tool should the engineer use to detect new files automatically, avoid processing the same file twice, and integrate smoothly with scalable data workflows?
A. Unity Catalog
B. Delta Lake
C. Databricks SQL
D. Data Explorer
E. Auto Loader
Answer: E
Explanation:
The scenario described involves processing files that are continuously added to a shared directory, where the system needs to detect new files automatically, avoid processing the same file twice, and integrate with scalable data workflows. The best tool for this scenario is Auto Loader.
Why E (Auto Loader) is correct:
Auto Loader is a feature in Databricks that simplifies the process of ingesting streaming data, particularly from files stored in cloud storage systems like Amazon S3, Azure Blob Storage, and others. It has the following key advantages:
Automatic Detection of New Files:
Auto Loader automatically detects new files that are added to a specified directory, ensuring that only new files are processed. This is done by monitoring the directory and identifying new files since the last successful run.Avoid Processing the Same File Twice:
Auto Loader efficiently tracks the files that have been processed, so it can avoid reprocessing files. This helps to ensure that no file is processed multiple times, which is crucial in scenarios where files cannot be deleted or modified.Scalable Data Workflows:
Auto Loader integrates seamlessly with Databricks and Delta Lake, enabling you to build scalable and robust data pipelines that can handle large volumes of incoming data with minimal operational overhead.
Why the other options are not correct:
A. Unity Catalog:
Unity Catalog is a data governance tool in Databricks that helps manage and secure access to data across various Databricks workspaces. It does not focus on file detection or processing, making it unsuitable for this specific scenario.B. Delta Lake:
Delta Lake provides features like ACID transactions and versioning for data lakes, and while it can store processed data efficiently, it does not inherently handle automatic detection of new files or the processing of new incoming data files from external systems. It works well with Auto Loader, but it doesn't handle the task by itself.C. Databricks SQL:
Databricks SQL is a query engine used to run SQL-based analytics and queries. It is not designed for file detection or handling incremental file processing tasks as required in this scenario.D. Data Explorer:
Data Explorer is a tool in Databricks for exploring and querying data. It is not designed for automating the detection of new files in a directory or managing the incremental loading of new files.
For the scenario where new files need to be detected automatically, processed incrementally, and where files cannot be deleted or modified by the pipeline, Auto Loader is the best tool. It integrates seamlessly with Databricks and provides scalable, reliable solutions for file ingestion, ensuring that only new files are processed without duplicates.
Thus, the correct answer is E.
Question 4:
In a Delta Live Tables (DLT) pipeline with three tables, data quality expectations are defined to automatically drop records that fail the quality checks. After running the pipeline, some records are dropped, but the engineer doesn't know which table is responsible for this.
What is the best approach for the engineer to pinpoint the table causing these records to be dropped?
A. Configure individual data quality expectations for each table during pipeline development.
B. It’s not possible to determine which table dropped the records in DLT.
C. Set up email alerts for records dropped due to quality violations.
D. Use the DLT pipeline UI to click on each table and examine detailed data quality statistics.
E. Click the “Error” button in the DLT interface to view and investigate current errors.
Answer: D
Explanation:
Delta Live Tables (DLT) is a robust framework within Databricks for building data pipelines that ensures data quality with its built-in quality expectations. When records are dropped due to quality violations, pinpointing the exact table responsible for the drops can be challenging if proper diagnostics aren't set up.
Why D is correct:
Use the DLT pipeline UI to click on each table and examine detailed data quality statistics.
The DLT pipeline UI provides a detailed overview of the pipeline's performance and data quality checks. By navigating through each table in the UI, you can access the data quality statistics for each table and identify which table caused the records to be dropped due to failed quality expectations.
This UI provides visibility into the statistics such as how many records were dropped due to quality violations in each table, making it easier to trace and troubleshoot data quality issues within the pipeline.
Why the other options are not correct:
A. Configure individual data quality expectations for each table during pipeline development.
While it's good practice to define individual data quality expectations for each table, this would not necessarily help pinpoint which table caused the issue after records have already been dropped. This would only improve granularity during development, not after the fact.B. It’s not possible to determine which table dropped the records in DLT.
This is incorrect. Delta Live Tables does provide ways to investigate the tables involved, such as through the UI, which gives visibility into which table is responsible for dropping records.C. Set up email alerts for records dropped due to quality violations.
Email alerts are a useful mechanism for notifying about issues, but they may not provide enough detail about which specific table caused the drop. The UI is a more direct method for tracing this kind of issue.E. Click the “Error” button in the DLT interface to view and investigate current errors.
The “Error” button is more suited to viewing pipeline errors or failures related to the pipeline itself (e.g., execution failures, syntax issues) rather than tracking which table caused records to be dropped due to data quality violations. Detailed data quality issues are best examined through the table-specific quality statistics available in the DLT UI.
To pinpoint which table is responsible for dropping records due to data quality issues, the most effective approach is to use the DLT pipeline UI, which provides detailed statistics and insights into the data quality checks for each table.
Thus, the correct answer is D.
Question 5:
A data engineer manages a scheduled job that runs a notebook every morning before starting their workday. They recently found an issue upstream that needs to be resolved before the notebook can execute. The engineer wants to add a new task that runs another notebook before the original one within the same job.
What is the best way to ensure the new task runs first?
A. Clone the existing task and modify it to execute the new notebook.
B. Add a new task to the current job and set it as a dependency for the original task.
C. Add a new task and set the original task as a dependency of the new task.
D. Create a new job and run both tasks concurrently.
E. Clone the existing task to a new job and modify it to run the new notebook.
Answer: C
Explanation:
The goal in this scenario is to ensure that a new task runs before the original task within the same job. The solution should respect the required task order within the job and allow the engineer to control the execution flow.
Why C is correct:
Add a new task and set the original task as a dependency of the new task.
By creating a new task and setting the original task as a dependency, the new task will run first. After the new task completes, the original task will then be executed.
This ensures that the issue upstream is resolved before the original notebook executes. Setting the dependency explicitly makes the new task a prerequisite for the original task.
Why the other options are not correct:
A. Clone the existing task and modify it to execute the new notebook.
Cloning the existing task and modifying it would mean that the engineer is essentially duplicating the task. However, this would not control the order of execution relative to the original task. The engineer still needs to manage task dependencies to ensure the correct sequence of operations.B. Add a new task to the current job and set it as a dependency for the original task.
This would make the new task dependent on the original task, meaning that the original task would need to complete before the new task runs. This is the opposite of the desired flow, where the new task needs to run first, not after the original task.D. Create a new job and run both tasks concurrently.
Running the tasks concurrently in two separate jobs would not guarantee the correct order of execution. The original task could run before the new task, potentially causing issues, as the new task should run first to address the upstream problem.E. Clone the existing task to a new job and modify it to run the new notebook.
Cloning the task into a new job is unnecessary and adds complexity. It doesn't resolve the issue within the same job or ensure the proper execution order. A new job would also result in unnecessary overhead for managing the job, especially when the goal is to keep both tasks in the same job.
To ensure the new task runs before the original task within the same job, setting the original task as a dependency for the new task ensures the correct execution sequence. This is the most effective solution to manage task execution order and resolve the issue before the original task runs.
Thus, the correct answer is C.
Question 6:
An engineering manager is tracking performance metrics for a new project on Databricks using a SQL query that runs every minute for the first week. The manager is concerned about the potential costs of keeping the compute resources running continuously beyond the week.
Which solution ensures the query stops after the first week to prevent additional charges?
A. Limit the number of DBUs consumed by the SQL endpoint.
B. Set the query’s refresh schedule to end after a specified number of refreshes.
C. There is no way to stop the query from incurring charges after the first week.
D. Restrict the number of users who can manage the query’s refresh schedule.
E. Set an end date for the query’s refresh schedule in the query scheduler.
Answer: E
Explanation:
To manage costs and ensure that the SQL query stops running after a certain period (in this case, after the first week), the best solution is to set an end date for the query's refresh schedule in the query scheduler. This will automatically stop the query from running once the set time has passed, preventing further execution and any associated charges.
Why E is correct:
Set an end date for the query’s refresh schedule in the query scheduler.
By setting an end date in the query scheduler, the manager ensures that the query automatically stops after the first week.
This method ensures that the query stops without requiring manual intervention, thereby controlling costs by avoiding unnecessary compute resources being consumed after the week has passed.
Why the other options are not correct:
A. Limit the number of DBUs consumed by the SQL endpoint.
While limiting DBUs (Databricks Unit) can help control resource usage, this doesn't directly stop the query from running after a week. The query may still continue executing, potentially incurring more costs, even if the DBU consumption is controlled. This solution is not effective for stopping the query after a set time.B. Set the query’s refresh schedule to end after a specified number of refreshes.
This could stop the query after a specific number of refreshes, but it is not as precise as setting an end date for the refresh schedule. It might not be as reliable, especially if the query refreshes more times than expected during the week.C. There is no way to stop the query from incurring charges after the first week.
This is incorrect. There are ways to control query execution and stop it after a set period (like setting an end date), so this option is not valid.D. Restrict the number of users who can manage the query’s refresh schedule.
Restricting access to the refresh schedule doesn't stop the query from executing. While limiting who can manage the query schedule is important for security, it doesn't automatically stop the query after a specific period.
The best way to ensure that the SQL query stops automatically after the first week and prevent further costs is to set an end date for the query's refresh schedule. This solution provides the desired automation to stop the query without requiring manual action after the set period.
Thus, the correct answer is E.
Question 7:
A data analysis team is facing performance issues due to high query latency when multiple users run small queries simultaneously on a shared Databricks SQL endpoint. The data engineering team has observed that all users are connecting to the same always-on SQL endpoint.
What is the most effective solution to improve query performance under concurrent loads?
A. Increase the cluster size of the SQL endpoint.
B. Expand the SQL endpoint’s maximum scaling range.
C. Enable the Auto Stop feature for the SQL endpoint.
D. Enable the Serverless feature for the SQL endpoint.
E. Enable the Serverless feature for the SQL endpoint and configure Spot Instances for “Reliability Optimized” mode.
Answer: D
Explanation:
The most effective way to improve query performance under concurrent loads in this scenario is to enable the Serverless feature for the SQL endpoint. This approach provides automatic scaling and better resource management, especially for handling varying query loads and reducing latency during simultaneous query executions by multiple users.
Why D is correct:
Enable the Serverless feature for the SQL endpoint.
The Serverless feature in Databricks SQL provides a highly scalable, fully managed environment that automatically adjusts the compute resources based on the query load.
It can handle varying numbers of concurrent queries without the need for manual scaling, making it more efficient and responsive under high-concurrency conditions.
Serverless SQL endpoints are ideal for workloads with unpredictable or fluctuating demand, where small queries are run by multiple users simultaneously.
With this approach, there is no need to manually manage cluster size or scaling range, as the system automatically adjusts resources in real-time based on demand.
Why the other options are not correct:
A. Increase the cluster size of the SQL endpoint.
While increasing the cluster size might help in some cases, it is less efficient in the long term because it would involve provisioning more compute resources even when there may not be a high demand for them. This leads to over-provisioning and unnecessary costs. Additionally, it doesn't scale dynamically based on usage, which is crucial in this case.B. Expand the SQL endpoint’s maximum scaling range.
Expanding the scaling range might improve performance for large queries or peak loads, but it still doesn't handle the issue of multiple small queries efficiently. The solution requires a more dynamic approach that scales with varying query loads, which Serverless SQL endpoints are better at providing.C. Enable the Auto Stop feature for the SQL endpoint.
Enabling Auto Stop is designed to stop idle clusters automatically, which helps save costs when the cluster isn't in use. However, it doesn't address the performance issues related to concurrent queries. If the SQL endpoint is still always-on and handling multiple simultaneous queries, Auto Stop won't improve the latency or performance under load.E. Enable the Serverless feature for the SQL endpoint and configure Spot Instances for “Reliability Optimized” mode.
While Serverless is indeed the correct approach to scale with varying query loads, configuring Spot Instances for “Reliability Optimized” mode might not be the best for SQL workloads that require consistent low-latency performance. Spot Instances can be terminated at any time, which could lead to unpredictable behavior for time-sensitive SQL queries, such as small queries being interrupted or delayed.
The Serverless feature provides the most effective and scalable solution to handle concurrent small queries, automatically adjusting resources as needed, which improves query performance. This is the most suitable approach in Databricks when dealing with high concurrency and low-latency requirements.
Thus, the correct answer is D.
Question 8:
A data engineer manages a Databricks SQL dashboard that requires a daily refresh. To reduce resource consumption and minimize costs, the engineer wants the associated SQL endpoint to only be active during the refresh and automatically shut down when not in use.
What is the most effective approach to minimize the SQL endpoint’s running time and costs?
A. Ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints.
B. Configure the dashboard to use a serverless SQL endpoint.
C. Enable the Auto Stop feature for the SQL endpoint.
D. Reduce the cluster size of the SQL endpoint.
E. Use a separate SQL endpoint for the dashboard, distinct from those used by other queries.
Answer: C
Explanation:
The best approach to minimize the SQL endpoint's running time and costs is to enable the Auto Stop feature for the SQL endpoint. The Auto Stop feature automatically stops the SQL endpoint after a specified period of inactivity, ensuring that compute resources are not unnecessarily running when not in use.
Why C is correct:
Enable the Auto Stop feature for the SQL endpoint.
The Auto Stop feature allows the SQL endpoint to automatically shut down after a defined idle period, which prevents the endpoint from running when it's not needed. This significantly reduces resource consumption and costs since the SQL endpoint will only be active when necessary (for the daily refresh in this case).
Since the engineer wants the SQL endpoint to be active only during the refresh and shut down afterward, this solution directly addresses that need by managing the endpoint's lifecycle based on activity.
Why the other options are not ideal:
A. Ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints.
This option does not directly address resource consumption or costs. Ensuring matching endpoints doesn't provide any mechanism for automatically stopping the endpoint when it's not in use, which is key for minimizing costs.B. Configure the dashboard to use a serverless SQL endpoint.
While serverless SQL endpoints can automatically scale to handle workload demands, they are designed for dynamic scaling of queries rather than explicitly managing when the endpoint should stop. In the context of minimizing costs and resource consumption, the Auto Stop feature in a standard SQL endpoint is a more cost-effective solution since the serverless model is intended for continuous, on-demand querying.D. Reduce the cluster size of the SQL endpoint.
Reducing the cluster size might decrease resource consumption but doesn't fully address the issue of automatically shutting down the SQL endpoint when not in use. The cluster could still be running unnecessarily during idle periods, leading to costs being incurred even when the dashboard isn’t active.E. Use a separate SQL endpoint for the dashboard, distinct from those used by other queries.
Using a separate SQL endpoint could help isolate the dashboard's workload from others, but it doesn’t address the primary issue of controlling when the endpoint is active. Without the Auto Stop feature or a similar mechanism, this option would still incur costs during idle periods.
The most effective solution for minimizing SQL endpoint running time and costs is to enable the Auto Stop feature. This ensures that the SQL endpoint will automatically shut down after the daily refresh, preventing unnecessary resource consumption and optimizing costs.
Thus, the best approach is C.
Question 9:
You are working on a Databricks SQL job that processes large datasets. To optimize query performance, you need to decide between using a traditional SQL endpoint or a serverless SQL endpoint. Which scenario is the best fit for using a serverless SQL endpoint?
A. When the SQL queries require high computational resources and consistent performance.
B. When the SQL queries are intermittent and don’t require a persistent endpoint running continuously.
C. When the SQL queries involve complex joins and aggregations.
D. When you need guaranteed low-latency access to data.
E. When the SQL queries process historical data that doesn’t change over time.
Answer: B
Explanation:
The best scenario for using a serverless SQL endpoint is when SQL queries are intermittent and do not require a persistent endpoint running continuously. Serverless SQL endpoints automatically scale up to handle the queries as needed, and they only use resources when queries are actively being processed. This is ideal for workloads where queries are sporadic or not constant, as there is no need to maintain an always-on SQL endpoint, which helps reduce costs and operational overhead.
Why B is correct:
When the SQL queries are intermittent and don’t require a persistent endpoint running continuously.
Serverless SQL endpoints are designed to provide dynamic, on-demand compute resources, which means they are suitable for situations where SQL queries are not run continuously but rather intermittently. This makes them cost-efficient because they only consume resources when queries are running, and they automatically scale based on the workload.
If the queries are intermittent and don’t need persistent infrastructure, a serverless endpoint is a perfect fit as it can shut down when not in use, avoiding the overhead of an always-on SQL endpoint.
Why the other options are less suitable:
A. When the SQL queries require high computational resources and consistent performance.
Serverless SQL endpoints are not optimized for heavy, resource-intensive workloads requiring consistent performance. For queries that demand high computational resources, a traditional SQL endpoint would be more appropriate, as it provides guaranteed resources for sustained performance.C. When the SQL queries involve complex joins and aggregations.
Complex queries involving large joins and aggregations benefit more from a traditional SQL endpoint, which is optimized for consistent performance and can be provisioned with sufficient computational resources to handle these types of operations. Serverless SQL endpoints, while scalable, are not ideal for sustained heavy workloads like complex queries.D. When you need guaranteed low-latency access to data.
Serverless SQL endpoints may not provide guaranteed low-latency access, as they dynamically scale based on demand. A traditional SQL endpoint would be better suited for applications where low-latency access is critical, as it maintains consistent performance without relying on scaling.E. When the SQL queries process historical data that doesn’t change over time.
Processing historical data can still be intermittent, but serverless SQL endpoints are generally used for workloads with more variability. If historical data is processed consistently and requires performance consistency, a traditional SQL endpoint is better suited to provide the necessary resources.
The serverless SQL endpoint is most suitable for situations where SQL queries are intermittent and do not require continuous running. This fits well with dynamic workloads that don’t need a persistent endpoint. Thus, B is the correct answer.
Question 10:
A data engineer is building a pipeline in Databricks that needs to handle streaming data from multiple sources. To ensure that the pipeline handles data efficiently, the engineer wants to use an incremental processing strategy.
Which Delta Live Tables feature should the engineer use to achieve this?
A. Use CREATE LIVE TABLE with batch processing mode.
B. Use CREATE STREAMING LIVE TABLE to enable incremental data processing.
C. Use Delta Lake’s OPTIMIZE command to manage the incremental data.
D. Use CREATE INCREMENTAL TABLE for streaming data ingestion.
E. Use CREATE STREAMING LIVE TABLE with a static data source for faster processing.
Answer: B
Explanation:
In Databricks, Delta Live Tables (DLT) allows you to process both batch and streaming data efficiently using the CREATE STREAMING LIVE TABLE syntax. This feature is specifically designed for incremental data processing, meaning it processes only the new data added since the last run, which is ideal for handling streaming data from multiple sources.
Why B is correct:
Use CREATE STREAMING LIVE TABLE to enable incremental data processing.
When you're working with streaming data, the CREATE STREAMING LIVE TABLE statement is the correct option. It allows you to process data incrementally, ensuring that only the new or updated data is ingested in each processing cycle, which makes it efficient and scalable.
Delta Live Tables with streaming mode ensure that the pipeline only processes new data and performs transformations incrementally, reducing overhead and making the pipeline more efficient in handling streaming data.
Why the other options are less suitable:
A. Use CREATE LIVE TABLE with batch processing mode.
CREATE LIVE TABLE is typically used for batch processing, not for handling streaming data. This mode processes data in predefined batches instead of continuously ingesting and processing data incrementally. It wouldn't be the best choice for streaming data where incremental processing is required.
C. Use Delta Lake’s OPTIMIZE command to manage the incremental data.
While the OPTIMIZE command is used to improve the performance of Delta Lake tables by compacting small files into larger ones, it is not specifically meant to handle incremental data processing. It is a performance optimization tool and not a data ingestion tool.
D. Use CREATE INCREMENTAL TABLE for streaming data ingestion.
CREATE INCREMENTAL TABLE is not a recognized Delta Live Tables feature. Delta Live Tables use CREATE STREAMING LIVE TABLE for managing incremental streaming data.
E. Use CREATE STREAMING LIVE TABLE with a static data source for faster processing.
While CREATE STREAMING LIVE TABLE is designed for streaming data, using a static data source goes against the principle of streaming data processing. A static data source would not provide the real-time data updates needed for a streaming pipeline, thus reducing the value of using a streaming table.
The CREATE STREAMING LIVE TABLE syntax is the correct choice when building pipelines that require efficient and incremental data processing, especially for handling streaming data from multiple sources. Therefore, B is the best answer.
