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

Section	Weight	Objectives
Topic 1: Productionizing Data Pipelines	18%	- Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling
Topic 2: Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions
Topic 3: Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution
Topic 4: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Workspace navigation and management 2. Compute options: clusters, SQL warehouses, serverless 3. Data layout and optimization: partitioning, file sizing, caching
Topic 5: Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Batch and streaming ingestion 3. Connecting to external data sources

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

A. Option A
B. Option E
C. Option C
D. Option B
E. Option D

Answer: A

Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.

NEW QUESTION # 179
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Data Explorer
B. Delta Live Tables
C. Unity Catalog
D. Auto Loader
E. Delta Lake

Answer: B

Explanation:
Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. References: Delta Live Tables Overview, Data Quality Monitoring

NEW QUESTION # 180
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

A. They can set up an Alert with a new webhook alert destination.
B. They can set up an Alert with a new email alert destination.
C. They can set up an Alert without notifications.
D. They can set up an Alert with one-time notifications.
E. They can set up an Alert with a custom template.

Answer: A

Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with
* Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.

NEW QUESTION # 181
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

A. python
(spark.readStream
.table("silver_table")
.groupBy("department")
.agg(avg("salary").alias("avg_salary"))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("complete")
.table("gold_table"))
B. python
(spark.readStream
.table("silver_table")
.groupBy("department")
.agg(avg("salary").alias("avg_salary"))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("append")
.table("gold_table"))
C. python
(spark.readStream
.table("raw_data")
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/bronze")
.outputMode("append")
.table("bronze_table"))
D. python
(spark.readStream
.table("silver_table")
.withColumn("full_name", concat(col("first_name"), lit(" "), col("last_name")))
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/gold")
.outputMode("append")
.table("gold_table"))
E. python
(spark.readStream
.table("bronze_table")
.filter(col("valid") == True)
.writeStream
.format("delta")
.option("checkpointLocation", "/checkpoint/silver")
.outputMode("append")
.table("silver_table"))

Answer: A

Explanation:
The best practice is to use " Complete " as output mode instead of " append " when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 182
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

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

Answer: A

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

NEW QUESTION # 183
......

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