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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Use PySpark for data processing - Work with Spark APIs in Python |
| Topic 2: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames |
| Topic 4: Data Pipeline Architecture | 15-20% | - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance - Implement incremental data processing |
| Topic 5: Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Explain Delta Lake features and benefits - Create and manage Delta tables - Understand ACID transactions and time travel |
| Topic 6: Spark SQL and DataFrames | 15-20% | - Write and execute Spark SQL queries - Join and union DataFrames - Aggregate and group data - Handle null values and data quality |
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NEW QUESTION # 146
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
Answer: A
Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution
NEW QUESTION # 147
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
Answer: D
Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.
NEW QUESTION # 148
An Auto Loader stream fails when a new column appears in incoming JSON files. The team wants the stream to add the new column automatically, restarting as needed, rather than failing permanently.
Which cloudFiles.schemaEvolutionMode setting should be used?
Answer: D
Explanation:
addNewColumns is the default mode when a schema location is provided: the stream fails on encountering an unknown column, updates the stored schema, and on restart processes data with the evolved schema. rescue keeps the schema fixed and places unexpected fields in the rescued data column, while none and failOnNewColumns do not evolve the schema.
NEW QUESTION # 149
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
Answer: B
Explanation:
Explanation
The GRANT statement is used to grant privileges on a database, table, or view to a user or role. The ALL PRIVILEGES option grants all possible privileges on the specified object, such as CREATE, SELECT, MODIFY, and USAGE. The syntax of the GRANT statement is:
GRANT privilege_type ON object TO user_or_role;
Therefore, to grant full permissions on the database customers to the new data engineering team, the command should be:
GRANT ALL PRIVILEGES ON DATABASE customers TO team;
NEW QUESTION # 150
Which of the following tools is used by Auto Loader process data incrementally?
Answer: E
Explanation:
Auto Loader provides a Structured Streaming source called cloudFiles that can process new data files as they arrive in cloud storage without any additional setup. Auto Loader uses a scalable key-value store to track ingestion progress and ensure exactly-once semantics. Auto Loader can ingest various file formats and load them into Delta Lake tables. Auto Loader is recommended for incremental data ingestion with Delta Live Tables, which extends the functionality of Structured Streaming and allows you to write declarative Python or SQL code to deploy a production-quality data pipeline. Reference: What is Auto Loader?, What is Auto Loader? | Databricks on AWS, Solved: How does Auto Loader ingest data? - Databricks - 5629
NEW QUESTION # 151
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