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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q291-Q296):

NEW QUESTION # 291
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can reduce the cluster size of the SQL endpoint.
B. They can set up the dashboard's SQL endpoint to be serverless.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: B

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. Reference:
Create a serverless SQL endpoint
Assign a SQL endpoint to a dashboard
SQL Analytics API

NEW QUESTION # 292
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

A. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
B. The Spark User interface has a debug tab that contains the variables that are used in this session.
C. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
D. The Data Engineer should add print statements to find out what the variable is.

Answer: C

NEW QUESTION # 293
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 clusters that are from a cluster pool
C. They can use jobs clusters instead of all-purpose clusters
D. They can configure the clusters to autoscale for larger data sizes
E. They can use endpoints available in Databricks SQL

Answer: B

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 # 294
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

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

Answer: D

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. Reference: 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 # 295
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

The data engineer only wants the query to process all of the available data in as many batches as required.
Which line of code should the data engineer use to fill in the blank?

A. trigger(processingTime="once")
B. trigger(availableNow=True)
C. trigger(once=True)
D. trigger(continuous="once")

Answer: B

NEW QUESTION # 296
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