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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Spark SQL and DataFrames | 15-20% | - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries - Handle null values and data quality |
| Topic 2: Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Write to and read from Delta tables - Create and manage Delta tables - Understand ACID transactions and time travel |
| Topic 3: Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Implement incremental data processing - Understand ELT vs ETL patterns |
| Topic 4: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing |
| Topic 5: Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts |
| Topic 6: Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) |
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NEW QUESTION # 74
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
Answer: E
Explanation:
Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. Reference: Schedule a job, Jobs API, Databricks CLI, Cron expressions
NEW QUESTION # 75
Which of the following describes the type of workloads that are always compatible with Auto Loader?
Answer: B
Explanation:
Explanation
Auto Loader is a feature of Databricks that simplifies and automates the process of loading streaming data into Delta Lake tables. Auto Loader can detect new and updated files in cloud storage and efficiently load them as micro-batches or as a continuous stream. Auto Loader is always compatible with streaming workloads, as it is designed to handle streaming sources such as Amazon S3, Azure Data Lake Storage Gen2, and Azure Blob Storage. The other types of workloads may or may not be compatible with Auto Loader, depending on the data source and the use case. References: The information can be referenced from Databricks documentation on Auto Loader: Auto Loader.
https://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-p
NEW QUESTION # 76
A data engineering team runs nightly batch ETL pipelines that transform large volumes of data stored in Delta Lake. The workloads are predictable and run on a fixed schedule. The team wants automatic scaling and cost efficiency, paying only for compute used during job execution. Which Databricks compute option is appropriate for this workload?
Answer: D
Explanation:
Job compute is designed for scheduled, automated workloads. It can autoscale for the ETL workload and terminates after the job finishes, avoiding the idle cost of persistent all-purpose compute.
NEW QUESTION # 77
Which languages are supported by Serverless compute clusters? (Choose two.)
Answer: C,D
Explanation:
Serverless compute clusters in Databricks support SQL (via Serverless SQL Warehouses) and Python (for interactive and job-based execution). Other languages like R, Scala, and Java require regular clusters.
NEW QUESTION # 78
A data engineer needs access to a table new_uable, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which approach can be used to identify the owner of new_table?
Answer: A
Explanation:
To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table.
References:Databricks documentation on Data Explorer: Using Data Explorer in Databricks
NEW QUESTION # 79
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