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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Delta Lake Fundamentals	20-25%	- Create and manage Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables
Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits
Data Pipeline Architecture	15-20%	- Implement incremental data processing - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance
Python for Data Engineering	10-15%	- Use PySpark for data processing - Work with Spark APIs in Python - Implement user-defined functions (UDFs)
Apache Spark Data Processing Fundamentals	20-25%	- Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Use Spark SQL for data processing
Spark SQL and DataFrames	15-20%	- Join and union DataFrames - Write and execute Spark SQL queries - Handle null values and data quality - Aggregate and group data

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
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 command can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT ON TABLE sales TO team;
B. grant all privileges on table sales TO team;
C. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
D. GRANT ALL PRIVILEGES ON TABLE team TO sales;

Answer: B

Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
Reference:
Databricks documentation on SQL permissions: SQL Permissions in Databricks

NEW QUESTION # 153
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace format("delta") with format("stream")
B. Replace "transactions" with the path to the location of the Delta table
C. Replace spark.read with spark.readStream
D. Replace predict with a stream-friendly prediction function
E. Replace schema(schema) with option ("maxFilesPerTrigger", 1)

Answer: C

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 154
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
B. They can schedule the query to run every 12 hours from the Jobs UI.
C. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
D. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
E. They can schedule the query to run every 1 day from the Jobs UI.

Answer: C

Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone. This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. Reference: Schedule a query, What are Databricks SQL alerts?, Jobs.

NEW QUESTION # 155
Which compute option should be chosen in a scenario where small-scale ad-hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

A. SQL Warehouse
B. Job Cluster
C. Serverless Compute
D. All-purpose Cluster

Answer: C

Explanation:
Serverless Compute is ideal for small-scale, frequent, ad-hoc Python scripts because it provisions automatically, scales quickly, and terminates when queries finish, minimizing operational overhead and costs.

NEW QUESTION # 156
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

A. if day_of_week = 1 and review_period = "True":
B. if day_of_week == 1 and review_period == "True":
C. if day_of_week = 1 & review_period: = "True":
D. if day_of_week = 1 and review_period:
E. if day_of_week == 1 and review_period:

Answer: E

Explanation:
In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. Reference: [Python Operators], [Python If ... Else]

NEW QUESTION # 157
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