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Databricks Certified Professional Data Engineer Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which of the following is a true statement about the global temporary view?

• A. A global temporary view persists even if the cluster is restarted
• B. A global temporary view is available only on the cluster it was created, when the cluster restarts global temporary view is automatically dropped.
• C. A global temporary view is automatically dropped after 7 days
• D. A global temporary view is stored in a user database
• E. A global temporary view is available on all clusters for a given workspace

Answer: B Explanation:
Explanation
The answer is, A global temporary view is available only on the cluster it was created.
Two types of temporary views can be created Session scoped and Global
A session scoped temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and re attached the temporary view is lost.
A global temporary view is available to all the notebooks in the cluster, if a cluster restarts global temporary view is lost.
NEW QUESTION # 143
Which of the statements are correct about lakehouse?

• A. Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads
• B. Lakehouse supports schema enforcement and evolution
• C. Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads
• D. Lakehouse does not support ACID
• E. In Lakehouse Storage and compute are coupled

Answer: B Explanation:
Explanation
The answer is Lakehouse supports schema enforcement and evolution,
Lakehouse using Delta lake can not only enforce a schema on write which is contrary to traditional big data systems that can only enforce a schema on read, it also supports evolving schema over time with the ability to control the evolution.
For example below is the Dataframe writer API and it supports three modes of enforcement and evolution, Default: Only enforcement, no changes are allowed and any schema drift/evolution will result in failure.
Merge: Flexible, supports enforcement and evolution
* New columns are added
* Evolves nested columns
* Supports evolving data types, like Byte to Short to Integer to Bigint How to enable:
* DF.write.format("delta").option("mergeSchema", "true").saveAsTable("tablename")
* or
* spark.databricks.delta.schema.autoMerge = True ## Spark session
Overwrite: No enforcement
* Dropping columns
* Change string to integer
* Rename columns
How to enable:
* DF.write.format("delta").option("overwriteSchema", "True").saveAsTable("tablename") What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated

NEW QUESTION # 144
You are currently working on a notebook that will populate a reporting table for downstream process consumption, this process needs to run on a schedule every hour, what type of cluster are you going to use to set up this job?

• A. Use delta live table pipeline to run in continuous mode
• B. The job cluster is best suited for this purpose.
• C. Since it's just a single job and we need to run every hour, we can use an all-purpose cluster
• D. Use Azure VM to read and write delta tables in Python

Answer: B Explanation:
Explanation
The answer is, The Job cluster is best suited for this purpose.
Since you don't need to interact with the notebook during the execution especially when it's a scheduled job, job cluster makes sense. Using an all-purpose cluster can be twice as expensive as a job cluster.
FYI,
When you run a job scheduler with option of creating a new cluster when the job is complete it terminates the cluster. You cannot restart a job cluster.
NEW QUESTION # 145
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 off the Auto Stop feature for the SQL endpoint to more than 30 mins.
• B. 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.
• C. 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.
• D. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy from
  "Cost optimized" to "Reliability Optimized."
• E. They can turn on the Serverless feature for the SQL endpoint.

Answer: C 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 # 146
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
_____
_____
LOCATION "dbfs:/mnt/sales/*.csv"

• A. FORMAT CSV
  FORMAT TYPE ( header ="true", delimiter = "|")
• B. FORMAT CSV
  OPTIONS ( "true","|")
• C. USING CSV
  TYPE ( "true","|")
• D. USING CSV
  OPTIONS ( header ="true", delimiter = "|")
  (Correct)
• E. FORMAT CSV
  TYPE ( header ="true", delimiter = "|")

Answer: D Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE tablename (colname1 coltyp1,..)
USING datasource
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"
NEW QUESTION # 147
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