Databricks-Certified-Data-Engineer-Associate Real Exam Answers & Detail Databricks-Certified-Data-Engineer-Associate Explanation

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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q290-Q295):

NEW QUESTION # 290
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?

A. All columns are created with the STRING data type.
B. Numeric columns are inferred as numeric types, while all other columns use STRING.
C. Table creation fails because column types are not specified.
D. Previously inferred column types are preserved.

Answer: A

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 # 291
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?

A. FROM CSV
B. None of these lines of code are needed to successfully complete the task
C. FROM "path/to/csv"
D. USING CSV
E. USING DELTA

Answer: D

NEW QUESTION # 292
A data engineer configures a Databricks Lakeflow Job for daily customer data processing:
- Entry task: Single notebook loads raw data
- Parallel tasks:
- SQL query task for cleansing
- Notebook task for feature engineering
- Pipeline task for model updates
- Exit task: Dashboard refresh (must run after all parallel tasks
complete)
- Requirement: Implement this dependency pattern using a DAG-based task graph Which task configuration ensures all parallel tasks complete before the dashboard refresh task runs?

A. Set the dashboard task to depend only on the SQL query task; other tasks run independently.
B. Configure the parallel tasks with sequential dependencies so each task waits for the previous one to finish.
C. Create separate Lakeflow Jobs for each parallel task and trigger them sequentially using external orchestration.
D. Add the dashboard task as dependent on all three parallel tasks using fan-in control flow.

Answer: D

Explanation:
A fan-in dependency makes the dashboard refresh task depend on all three parallel tasks.
Databricks runs the parallel tasks after the entry task, and the dashboard refresh starts only after every upstream dependency completes.

NEW QUESTION # 293
A Databricks single-task workflow fails due to an error in a notebook. The data engineer fixes the mistake in the notebook.
What should the data engineer do to rerun the workflow?

A. Swap the cluster
B. Restart the Cluster
C. Repair the task
D. Repair the run

Answer: D

Explanation:
Rerunning a failed workflow after fixing the notebook requires repairing the run so the task is re- executed with the updated code, without recreating the entire workflow or changing cluster configuration.

NEW QUESTION # 294
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

A. They can configure the clusters to be single-node
B. They can use endpoints available in Databricks SQL
C. They can use jobs clusters instead of all-purpose clusters
D. They can use clusters that are from a cluster pool
E. They can configure the clusters to autoscale for larger data sizes

Answer: D

Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 295
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