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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Passing Score: | 70% |
| Related Certifications: | Databricks Certified Data Analyst Associate |
| Real Exam Qty: | 60 |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Sample Questions: | Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | Recommended: 6+ months of experience with Databricks and Apache Spark |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
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The GAQM Databricks-Certified-Data-Engineer-Associate exam is designed to validate the skills and knowledge of individuals working with the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is a valuable asset for data engineers looking to advance their careers and demonstrate their expertise in the field of big data and analytics. Databricks-Certified-Data-Engineer-Associate Exam Tests candidates on a range of skills, including data modeling, data ingestion, data processing, and data analysis.
NEW QUESTION # 73
An Auto Loader stream fails when a new column appears in incoming JSON files. The team wants the stream to add the new column automatically, restarting as needed, rather than failing permanently.
Which cloudFiles.schemaEvolutionMode setting should be used?
Answer: D
Explanation:
addNewColumns is the default mode when a schema location is provided: the stream fails on encountering an unknown column, updates the stored schema, and on restart processes data with the evolved schema. rescue keeps the schema fixed and places unexpected fields in the rescued data column, while none and failOnNewColumns do not evolve the schema.
NEW QUESTION # 74
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?
Answer: C
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 # 75
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
Answer: C
Explanation:
Explanation
https://docs.databricks.com/sql/admin/transfer-ownership.html
NEW QUESTION # 76
A data engineering team needs to incrementally ingest customer transactions from a SaaS application into the Databricks Data Intelligence Platform with the following capabilities:
Built-in change data capture, including updates and deletes
Automatic schema evolution
Serverless execution with retries and minimal maintenance
OAuth support and basic monitoring
Which solution meets all the requirements?
Answer: D
Explanation:
Option D satisfies the requirements because a Lakeflow Connect managed SaaS connector provides source-specific authentication, incremental ingestion, schema evolution, and automated retries as managed capabilities. Managed connectors use serverless infrastructure and publish governed destination tables that can be processed downstream with Lakeflow Spark Declarative Pipelines. This avoids custom code for OAuth token handling, API pagination, CDC state management, retry behavior, and schema-change recovery. Option A still requires the engineering team to implement and maintain CDC and operational logic. Options B and C place most authentication, schema evolution, change tracking, and failure recovery responsibilities in custom notebooks or jobs, conflicting with the minimal-maintenance requirement. Therefore, the fully managed connector described in option D is the only solution that delivers the requested ingestion and operational behavior as an integrated platform capability.
NEW QUESTION # 77
A developer is building a data pipeline that processes records from a Bronze table into a Silver table. The Bronze table, bronze_events, contains duplicate records because of at-least-once delivery guarantees from the upstream ingestion system. The developer writes the following PySpark code:
deduped_df = df.dropDuplicates()
deduped_df.summary("count", "mean", "stddev").show()
from pyspark.sql.functions import approx_count_distinct
deduped_df.select(approx_count_distinct("user_id")).show()
After running dropDuplicates() without arguments, some rows that differ only in the event_timestamp column remain. The developer wants to deduplicate records based only on user_id and event_type, keeping one row for each unique combination of those two columns.
Which code change achieves this deduplication requirement?
Answer: A
NEW QUESTION # 78
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