Databricks-Certified-Data-Engineer-Associate Reliable Test Pattern & Databricks-Certified-Data-Engineer-Associate Valid Exam Question

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

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

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Databricks-Certified-Data-Engineer-Associate Reliable Test Pattern - Free PDF 2026 Databricks Databricks-Certified-Data-Engineer-Associate First-grade Valid Exam Question

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

NEW QUESTION # 290
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 spark.read with spark.readStream
C. Replace "transactions" with the path to the location of the Delta table
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace predict with a stream-friendly prediction function

Answer: B

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 # 291
A data engineer at a company that uses Databricks with Unity Catalog needs to share a collection of tables with an external partner who also uses a Databricks workspace enabled for Unity Catalog. The data engineer decides to use Delta Sharing to accomplish this. What is the first piece of information the data engineer should request from the external partner to set up Delta Sharing?

A. The IP address of their Databricks workspace
B. The sharing identifier of their Unity Catalog metastore
C. Their Databricks account password
D. The name of their Databricks cluster

Answer: B

Explanation:
To configure Delta Sharing between two Databricks workspaces with Unity Catalog, the first requirement is the sharing identifier of the partner's Unity Catalog metastore, which enables secure setup of the share and recipient relationship.

NEW QUESTION # 292
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

A. They can turn on the Serverless feature for the SQL endpoint.
B. They can increase the cluster size of the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range.
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."

Answer: A

NEW QUESTION # 293
A team is designing their Data bricks Asset Bundle for deployment. They want to ensure their bundle includes all necessary resources, targeted environment configurations, and deployment settings in a single version-controlled file. What is required as the main configuration file at the root of their project?

A. A JSON file named bundle_config.json that stores all resource details and environment variables
B. A directory with multiple TXT files, each describing a single resource for the bundle
C. A databricks.yml YAML file that contains the bundle's name, targets, includes resources, and variables
D. A Python script called configure_bundle.py that dynamically creates resources at runtime

Answer: C

Explanation:
Databricks Asset Bundles require a databricks.yml file at the project root, which defines the bundle configuration including targets, resources, variables, and deployment settings in a single version-controlled specification.

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

Answer: E

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.
:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 295
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