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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: 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 |
| Topic 3: Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Create and manage Delta tables - Write to and read from Delta tables |
| Topic 4: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Aggregate and group data - Write and execute Spark SQL queries - Join and union DataFrames |
| Topic 5: Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing |
| Topic 6: Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs) |
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NEW QUESTION # 193
A data engineer needs Task C to run only when Task A succeeds and Task B fails.
Which dependency configuration implements this conditional logic?
Answer: C
NEW QUESTION # 194
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: B
Explanation:
1: In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver
NEW QUESTION # 195
Which of the following is stored in the Databricks customer's cloud account?
Answer: B
Explanation:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure.
Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
Databricks Architecture
Databricks Data Sources
Databricks Repos
[Databricks Notebooks]
[Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 196
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?
Answer: E
Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.
DataStreamWriter.trigger.html
NEW QUESTION # 197
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
Answer: E
Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql
NEW QUESTION # 198
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