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

Section	Objectives
Topic 1: Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Topic 2: Data Governance and Quality	- Unity Catalog basics - Data quality concepts and management - Data access control and governance
Topic 3: Data Processing and Transformations	- PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization)
Topic 4: Productionizing Data Pipelines	- Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration
Topic 5: Data Ingestion and ELT Development	- ETL patterns and transformations - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

A. Driver node
B. Databricks Filesystem
C. Worker node
D. JDBC data source
E. Databricks web application

Answer: E

Explanation:
The control plane in the classic Databricks architecture is responsible for managing the Databricks workspace, user and group management, and cluster management, among other things. The Databricks web application is a part of the control plane that enables users to interact with the workspace, create and manage clusters, and work with notebooks, jobs, and data.
Worker nodes and driver nodes are part of the data plane, which is responsible for executing data processing tasks. JDBC data sources and the Databricks Filesystem are services that are used by both the control plane and the data plane.

NEW QUESTION # 171
A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently encounters performance bottlenecks. Based on developer feedback, the data engineer assumes the job is low on compute resources. To pinpoint the issue, the data engineer observes the Spark Ul and finds out the job has a high CPU time vs Task time.
Which course of action should the data engineer take?

A. High CPU time vs Task time means a CPU over-utilized job. The data engineer may need to consider executor and core tuning or resizing the cluster
B. High CPU time vs Task time means efficient use of cluster and no change needed
C. High CPU time vs Task time means an under-utilized cluster. The data engineer may need to repartition data to spread the jobs more evenly throughout the cluster.
D. High CPU time vs Task time means over-utilized memory and the need to increase parallelism

Answer: A

NEW QUESTION # 172
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 schema(schema) with option ("maxFilesPerTrigger", 1)
B. Replace spark.read with spark.readStream
C. Replace format("delta") with format("stream")
D. Replace predict with a stream-friendly prediction function
E. Replace "transactions" with the path to the location of the Delta table

Answer: B

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html

NEW QUESTION # 173
A notebook task named check_volume calculates a daily data volume and writes it to a Lakeflow Jobs task value named record_count. If the count is greater than 1 million, the job should run high_volume_pipeline; otherwise, it should run normal_pipeline.
Which control-flow feature should be used?

A. Use a For each task over record_count and stop when the value is below the threshold.
B. Add an If/else condition task that reads the record_count task value and routes execution to different downstream tasks.
C. Configure Run if conditions based purely on task status.
D. Configure additional retries for check_volume until the count exceeds the threshold.

Answer: B

NEW QUESTION # 174
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

A. DROP
B. MERGE
C. INSERT
D. APPEND
E. IGNORE

Answer: B

Explanation:
The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. References: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE
| Databricks on AWS

NEW QUESTION # 175
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