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The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is a highly sought-after certification for individuals who want to demonstrate their expertise in building reliable, scalable, and performant data pipelines using Databricks. This certification is designed to validate the skills and knowledge required to design, implement, and maintain data pipelines for big data processing using Databricks.
The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for data professionals who want to validate their skills and knowledge in building and deploying data engineering solutions using Databricks. Databricks is a unified data analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data projects. The certification exam covers a range of topics such as data ingestion, data processing, data transformation, and data storage using Databricks.
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The Databricks Certified Professional Data Engineer exam is designed to test the skills and knowledge of individuals who work with big data and cloud computing technologies. This exam is primarily focused on assessing candidates’ abilities to design, build, and maintain big data solutions using the Apache Spark platform. This certification is highly valued in the industry and can help individuals demonstrate their proficiency in managing big data projects.
Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):
NEW QUESTION # 104
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:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?
- A. .trigger(processingTime="once")
- B. .processingTime(1)
- C. .processingTime("once")
- D. .trigger(continuous="once")
- E. .trigger(once=True)
Answer: E
NEW QUESTION # 105
Which of the following results in the creation of an external table?
- A. CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL
- B. CREATE TABLE transactions (id int, desc string)
- C. CREATE TABLE transactions (id int, desc string) LOCATION '/mnt/delta/transactions'
- D. CREATE EXTERNAL TABLE transactions (id int, desc string)
- E. CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL
Answer: C
Explanation:
Explanation
Answer is CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION
'/mnt/delta/transactions'
Anytime a table is created using Location it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/"
NEW QUESTION # 106
A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?
- A. A data lakehouse fully exists in the cloud
- B. A data lakehouse provides autoscaling for compute clusters
- C. A data lakehouse requires very little data modeling
- D. A data lakehouse combines compute and storage for simple governance
- E. A data lakehouse stores unstructured data and is ACID-compliant
Answer: E
NEW QUESTION # 107
Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?
- A. The ability to declare and maintain data table dependencies
- B. The ability to perform batch and streaming queries
- C. The ability to access previous versions of data tables
- D. The ability to automatically scale compute resources
- E. The ability to write pipelines in Python and/or SQL
Answer: A
NEW QUESTION # 108
The research team has put together a funnel analysis query to monitor the customer traffic on the e-commerce platform, the query takes about 30 mins to run on a small SQL endpoint cluster with max scaling set to 1 cluster. What steps can be taken to improve the performance of the query?
- A. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy from
"Cost optimized" to "Reliability Optimized." - B. They can increase the cluster size anywhere from X small to 3XL to review the per-formance and select the size that meets the required SLA.
- C. They can increase the maximum bound of the SQL endpoint's scaling range anywhere from between 1 to 100 to review the performance and select the size that meets the re-quired SLA.
- D. They can turn on the Serverless feature for the SQL endpoint.
- E. They can turn off the Auto Stop feature for the SQL endpoint to more than 30 mins.
Answer: B
Explanation:
Explanation
The answer is, They can increase the cluster size anywhere from 2X-Small to 4XL(Scale Up) to review the performance and select the size that meets your SLA. If you are trying to improve the performance of a single query at a time having additional memory, additional worker nodes mean that more tasks can run in a cluster which will improve the performance of that query.
The question is looking to test your ability to know how to scale a SQL Endpoint(SQL Warehouse) and you have to look for cue words or need to understand if the queries are running sequentially or concurrently. if the queries are running sequentially then scale up(Size of the cluster from 2X-Small to 4X-Large) if the queries are running concurrently or with more users then scale out(add more clusters).
SQL Endpoint(SQL Warehouse) Overview: (Please read all of the below points and the below diagram to understand )
1.A SQL Warehouse should have at least one cluster
2.A cluster comprises one driver node and one or many worker nodes
3.No of worker nodes in a cluster is determined by the size of the cluster (2X -Small ->1 worker, X-Small ->2 workers.... up to 4X-Large -> 128 workers) this is called Scale Up
4.A single cluster irrespective of cluster size(2X-Smal.. to ...4XLarge) can only run 10 queries at any given time if a user submits 20 queries all at once to a warehouse with 3X-Large cluster size and cluster scaling (min
1, max1) while 10 queries will start running the remaining 10 queries wait in a queue for these 10 to finish.
5.Increasing the Warehouse cluster size can improve the performance of a query, example if a query runs for 1 minute in a 2X-Small warehouse size, it may run in 30 Seconds if we change the warehouse size to X-Small.
this is due to 2X-Small has 1 worker node and X-Small has 2 worker nodes so the query has more tasks and runs faster (note: this is an ideal case example, the scalability of a query performance depends on many factors, it can not always be linear)
6.A warehouse can have more than one cluster this is called Scale Out. If a warehouse is configured with X-Small cluster size with cluster scaling(Min1, Max 2) Databricks spins up an additional cluster if it detects queries are waiting in the queue, If a warehouse is configured to run 2 clusters(Min1, Max 2), and let's say a user submits 20 queries, 10 queriers will start running and holds the remaining in the queue and databricks will automatically start the second cluster and starts redirecting the 10 queries waiting in the queue to the second cluster.
7.A single query will not span more than one cluster, once a query is submitted to a cluster it will remain in that cluster until the query execution finishes irrespective of how many clusters are available to scale.
Please review the below diagram to understand the above concepts:
Scale-up-> Increase the size of the SQL endpoint, change cluster size from 2X-Small to up to 4X-Large If you are trying to improve the performance of a single query having additional memory, additional worker nodes and cores will result in more tasks running in the cluster will ultimately improve the performance.
During the warehouse creation or after, you have the ability to change the warehouse size (2X-Small....to
...4XLarge) to improve query performance and the maximize scaling range to add more clusters on a SQL Endpoint(SQL Warehouse) scale-out if you are changing an existing warehouse you may have to restart the warehouse to make the changes effective.
NEW QUESTION # 109
......
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