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| Section | Weight | Objectives |
|---|---|---|
| Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Write and execute Spark SQL queries - Aggregate and group data - Join and union DataFrames |
| Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Implement incremental data processing - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns |
| Python for Data Engineering | 10-15% | - Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python |
| Lakehouse Platform Concepts | 10-15% | - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits |
| Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables - Understand ACID transactions and time travel |
| Apache Spark Data Processing Fundamentals | 20-25% | - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Use Spark SQL for data processing |
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NEW QUESTION # 65
A data engineer has realized that they made a mistake when making a daily update to a table.
They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
Answer: D
Explanation:
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUM with certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.
NEW QUESTION # 66
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?
Answer: E
Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions- from-query/td-p/36696
NEW QUESTION # 67
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: D
NEW QUESTION # 68
A data engineer uploads a CSV file using the Create or modify a table using file upload option in Databricks. To avoid incorrect schema inference, the engineer disables Automatically detect column types before creating a Unity Catalog-managed table.
What is the outcome?
Answer: B
Explanation:
When Automatically detect column types is disabled during file upload, Databricks does not infer specialized data types from the CSV values. Instead, every uploaded column is created as STRING. This behavior prevents values that resemble numbers, dates, or Boolean values from being automatically assigned potentially incorrect types. The engineer can subsequently transform or cast individual columns into the required target types as part of a controlled Silver-layer process. Option B is incorrect because partial numeric inference does not occur when automatic detection is disabled. Option C is incorrect because Databricks can create the table without manually supplied types by using STRING for every column. Option D is also incorrect because disabling detection determines the schema behavior for the table creation operation. Therefore, option A accurately describes the result.
NEW QUESTION # 69
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
Answer: C
Explanation:
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. Reference: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks
NEW QUESTION # 70
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