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| Section | Objectives |
|---|
| Productionizing Data Pipelines | - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization
|
| Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data
|
| Data Processing and Transformations | - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs)
|
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
|
| Data Governance and Quality | - Data quality concepts and management - Data access control and governance - Unity Catalog basics
|
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q291-Q296):
NEW QUESTION # 291
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark UI and can see the ERRORS as follows:
"java.lang.OutofMemoryError: Java heap space"
Which two corrective actions should the data engineer perform to resolve this issue? (Choose two.)
- A. Upsize the driver node and deactivate autoshuffle partitions
- B. Upsize the worker nodes and activate autoshuffle partitions
- C. Fix the shuffle partitions to 50 to ensure the allocation
- D. Narrow the filters in order to collect less data in the query
- E. Cache the dataset in order to boost the query performance
Answer: B,D
Explanation:
Reducing input with narrower filters lowers memory pressure, and increasing executor resources (upsize worker nodes) plus enabling adaptive/auto shuffle partitioning provides more memory and better partitioning to avoid Java heap OOM during Spark SQL execution.
NEW QUESTION # 292
A data engineer is designing a Databricks Job that includes a Notebook task for data cleansing and a SQL Query task for generating a summary report. The summary report must only execute after the cleansing task completes successfully. How should the dependency be configured in the Lakeflow Jobs task graph?
- A. Set both tasks to run on the same schedule with a five-minute start delay for the report.
- B. Select the cleansing task as a 'Run if' dependency in the summary report task configuration.
- C. Place both tasks in the same notebook and use a standard Python function call.
- D. Configure the cleansing task to use an 'On Failure' trigger to start the summary report.
Answer: B
Explanation:
Configure the summary report task to depend on the cleansing task and use the default Run if: All succeeded condition. This ensures the SQL task runs only after the notebook task completes successfully.
NEW QUESTION # 293
A data engineer wants to create an external table in Databricks that references data stored in an Azure Data Lake Storage (ADLS) location. The goal is to enable Databricks to access and query this external data without moving it into the Databricks-managed storage. Which step should the data engineer take to successfully create the external table?
- A. Use the CREATE TABLE statement and specify the LOCATION clause with the path to the external data.
- B. CREATE UNMANAGED TABLE statement without specifying a LOCATION clause.
- C. CREATE EXTERNAL TABLE statement without specifying a LOCATION clause.
- D. Use the CREATE MANAGED TABLE statement and specify the LOCATION clause with the path to the external data.
Answer: A
Explanation:
To reference data stored outside of Databricks-managed storage, the engineer should use CREATE TABLE ... LOCATION 'path', which creates an unmanaged (external) table pointing to the ADLS data without moving it into Databricks storage.
NEW QUESTION # 294
What is stored in a Databricks customer's cloud account?
- A. Data
- B. Notebooks
- C. Databricks web application
- D. Cluster management metadata
Answer: A
Explanation:
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.
Reference:
Databricks documentation: Data in Databricks
NEW QUESTION # 295
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 & review_period: = "True":
- C. if day_of_week == 1 and review_period == "True":
- D. if day_of_week == 1 and review_period:
- E. if day_of_week = 1 and review_period:
Answer: D
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. References: [Python Operators], [Python If ... Else]
NEW QUESTION # 296
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