2024 Provide Updated Databricks Databricks-Certified-Professional-Data-Engineer Dumps as Practice Test and PDF
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Databricks Certified Professional Data Engineer exam covers a wide range of topics, such as data ingestion, transformation, storage, and processing. Databricks-Certified-Professional-Data-Engineer exam tests the candidates' ability to use Databricks tools and technologies to solve real-world problems and challenges. Candidates who pass Databricks-Certified-Professional-Data-Engineer exam demonstrate their proficiency in designing, building, and managing data pipelines with Databricks, which is a leading cloud-based platform for big data processing and analytics.
NEW QUESTION # 16
Which of the following is true, when building a Databricks SQL dashboard?
- A. A dashboard can only use results from one query
- B. A dashboard can only have one refresh schedule
- C. More than one visualization can be developed using a single query result
- D. Only one visualization can be developed with one query result
- E. A dashboard can only connect to one schema/Database
Answer: C
Explanation:
Explanation
the answer is, More than one visualization can be developed using a single query result.
In the query editor pane + Add visualization tab can be used for many visualizations for a single query result.
Graphical user interface, text, application Description automatically generated
NEW QUESTION # 17
Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos.
- A. Create and edit code
- B. Create a new branch
- C. Trigger Databricks Repos pull API to update the latest version
- D. Commit and push code
- E. Pull request and review process
Answer: E
Explanation:
Explanation
The answer is Pull request and review process, please note: the question is asking for steps that are being implemented in GIT provider not Databricks Repos.
See below diagram to understand the role of Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram Description automatically generated
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NEW QUESTION # 18
You are asked to set up an alert to notify in an email every time a KPI indicater increases beyond a threshold value, team also asked you to include the actual value in the alert email notification.
- A. Setup an alert but use the default template to notify the message in email's subject
- B. Use the webhook destination instead so alert message can be customized
- C. Use notebook and python code to run every minute, using python variables to capture send the information in an email
- D. Use custom email hook to customize the message
- E. Setup an alert but use the custom template to notify the message in email's subject
Answer: E
Explanation:
Explanation
Alerts support custom template supports using variables to customize the default message, set up the query to compare the KPI current value to the threshold and use the variable QUE-RY_RESULT_VALUE to display the value in the email notification.
here is a simple alert, that uses variables in the custom template to present these values in the email notification message, when the alert is fired these variables get replaced with actual values.
Alert with custom template
Graphical user interface, application Description automatically generated
When you enable preview you can see how the alert looks when you substitute the variables.
Graphical user interface, text, application, email Description automatically generated
Below are additional template variables available to you with the custom template.
Alerts | Databricks on AWS
Graphical user interface, text, application, email Description automatically generated
NEW QUESTION # 19
You have noticed that Databricks SQL queries are running slow, you are asked to look reason why queries are running slow and identify steps to improve the performance, when you looked at the issue you noticed all the queries are running in parallel and using a SQL endpoint(SQL Warehouse) with a single cluster. Which of the following steps can be taken to improve the performance/response times of the queries?
*Please note Databricks recently renamed SQL endpoint to SQL warehouse.
- A. They can turn on the Serverless feature for the SQL endpoint(SQL warehouse) and change the Spot Instance Policy to "Reliability Optimized."
- B. They can increase the maximum bound of the SQL endpoint(SQL warehouse)'s scaling range
- C. They can increase the warehouse size from 2X-Smal to 4XLarge of the SQL end-point(SQL warehouse).
- D. They can turn on the Serverless feature for the SQL endpoint(SQL warehouse).
- E. They can turn on the Auto Stop feature for the SQL endpoint(SQL warehouse).
Answer: B
Explanation:
Explanation
The answer is, They can increase the maximum bound of the SQL endpoint's scaling range when you increase the max scaling range more clusters are added so queries instead of waiting in the queue can start running using available clusters, see below for more explanation.
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, for 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 having 1 worker node and X-Small having 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 con-figured 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:
SQL endpoint(SQL Warehouse) scales horizontally(scale-out) and vertical (scale-up), you have to understand when to use what.
Scale-out -> to add more clusters for a SQL endpoint, change max number of clusters If you are trying to improve the throughput, being able to run as many queries as possible then having an additional cluster(s) will improve the performance.
Databricks SQL automatically scales as soon as it detects queries are in queuing state, in this example scaling is set for min 1 and max 3 which means the warehouse can add three clusters if it detects queries are waiting.
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.
How do you know how many clusters you need(How to set Max cluster size)?
When you click on an existing warehouse and select the monitoring tab, you can see warehouse utilization information(see below), there are two graphs that provide important information on how the warehouse is being utilized, if you see queries are being queued that means your warehouse can benefit from additional clusters. Please review the additional DBU cost associated with adding clusters so you can take a well balanced decision between cost and performance.
NEW QUESTION # 20
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?
- A. Logistic regression
- B. Decision trees
- C. TF-IDF
- D. Linear regression
Answer: D
NEW QUESTION # 21
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple times?
- A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
- B. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
- C. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
- D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
- E. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
Answer: E
Explanation:
Explanation
This is the correct answer because it describes the execution and results of running the above query multiple times. The query uses the readChanges function to read all change events from a bronze table that has enabled change data feed. The readChanges function takes two arguments: version and options. The version argument specifies which version of the table to read changes from, and can be either a specific version number or -1 to indicate all versions. The options argument specifies additional options for reading changes, such as whether to include deletes or not. In this case, the query reads all changes from all versions of the bronze table and filters out delete events by setting includeDeletes to false. Then, it uses write.format("delta").mode("overwrite") to overwrite a target table using the entire history of inserted or updated records, giving the desired result of a Type 1 table representing all values that have ever been valid for all rows in the bronze table. Verified References: [Databricks Certified Data Engineer Professional], under
"Delta Lake" section; Databricks Documentation, under "Read changes in batch queries" section.
NEW QUESTION # 22
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
- A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
- B. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
- C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
- D. The source query failed to update properly for three consecutive minutes and then restarted
- E. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
Answer: C
Explanation:
Explanation
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.
NEW QUESTION # 23
Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?
- A. Disable the auto-scaling feature
- B. Use High concurrency mode instead of the standard mode
- C. Increase the size of the driver node
- D. Setup multiple clusters so each team member has their own cluster
Answer: B
Explanation:
Explanation
The answer is Use High concurrency mode instead of the standard mode,
https://docs.databricks.com/clusters/cluster-config-best-practices.html#cluster-mode High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs.
Databricks recommends enabling autoscaling for High Concurrency clusters.
NEW QUESTION # 24
A newly joined team member John Smith in the Marketing team who currently does not have any access to the data requires read access to customers table, which of the following statements can be used to grant access.
- A. GRANT SELECT, USAGE ON TABLE customers TO [email protected]
- B. GRANT READ, USAGE ON customers TO [email protected]
- C. GRANT SELECT, USAGE TO [email protected] ON TABLE customers
- D. GRANT READ, USAGE TO [email protected] ON TABLE customers
- E. GRANT READ, USAGE ON TABLE customers TO [email protected]
Answer: A
Explanation:
Explanation
The answer is GRANT SELECT, USAGE ON TABLE customers TO [email protected] Data object privileges - Azure Databricks | Microsoft Docs
NEW QUESTION # 25
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. await("event_time + '10 minutes'")
- B. delayWrite("event_time", "10 minutes")
- C. awaitArrival("event_time", "10 minutes")
- D. slidingWindow("event_time", "10 minutes")
- E. withWatermark("event_time", "10 minutes")
Answer: E
Explanation:
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html
NEW QUESTION # 26
Which of the following Structured Streaming queries successfully performs a hop from a Silver to Gold table?
- A. 1.(spark.table("sales")
2..groupBy("store")
3..agg(sum("sales"))
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("complete")
7..table("aggregatedSales") )
(Correct) - B. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") ) - C. 1.(spark.table("sales")
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("complete")
5..table("sales") ) - D. 1.(spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales") ) - E. 1.(spark.table("sales")
2..withColumn("avgPrice", col("sales") / col("units"))
3..writeStream
4..option("checkpointLocation", checkpointPath)
5..outputMode("append")
6..table("cleanedSales") )
Answer: A
Explanation:
Explanation
The answer is
1.(spark.table("sales")
2..groupBy("store")
3..agg(sum("sales"))
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("complete")
7..table("aggregatedSales") )
The gold layer is normally used to store aggregated data
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence
NEW QUESTION # 27
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?
- A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
- B. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
- C. When tables are created, make sure that the external keyword is used in the create table statement.
- D. Whenever a database is being created, make sure that the location keyword is used
- E. Whenever a table is being created, make sure that the location keyword is used.
Answer: E
NEW QUESTION # 28
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students
suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I
pick a French student, it will be a girl, that is, find P(A|B).
- A. 1/6
- B. 1/3
- C. 2/6
- D. 2/3
Answer: A
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION # 29
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
- A. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
- B. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
- C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
- D. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
- E. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
Answer: D
Explanation:
Explanation
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE
NEW QUESTION # 30
Which of the following python statements can be used to replace the schema name and table name in the query?
- A. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from schema_name.table_name" - B. 1.table_name = "sales"
2.query = "select * from {schema_name}.{table_name}" - C. 1.table_name = "sales"
2.query = f"select * from + schema_name +"."+table_name" - D. 1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
Answer: D
Explanation:
Explanation
The answer is
1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
It is always best to use f strings to replace python variables, rather than using string concatenation.
NEW QUESTION # 31
A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?
- A. They can utilize multiple tasks in a single job with a linear dependency
- B. They can set up a retry policy on the first Job to help it run more quickly
- C. They can limit the size of the output in the second Job so that it will not fail as easily
- D. They can set up the data to stream from the first Job to the second Job
- E. They can use cluster pools to help the Jobs run more efficiently
Answer: A
NEW QUESTION # 32
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?
- A. CREATE DATABASE IF NOT EXISTS customer360;
- B. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
- C. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
- D. CREATE DATABASE customer360 LOCATION '/customer/customer360';
- E. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
Answer: E
NEW QUESTION # 33
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables.
Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?
- A. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
- B. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
- C. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
- D. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
- E. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
Answer: A
Explanation:
Explanation
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "Database object privileges" section.
NEW QUESTION # 34
Which of the following array functions takes input column return unique list of values in an array?
- A. COLLECT_LIST
- B. COLLECT_SET
- C. ARRAY_INTERSECT
- D. COLLECT_UNION
- E. ARRAY_UNION
Answer: B
Explanation:
Explanation
Table Description automatically generated
NEW QUESTION # 35
When using the complete mode to write stream data, how does it impact the target table?
- A. Target table is overwritten for each batch
- B. Entire stream waits for complete data to write
- C. Stream must complete to write the data
- D. Delta commits transaction once the stream is stopped
- E. Target table cannot be updated while stream is pending
Answer: A
Explanation:
Explanation
The answer is Target table is overwritten for each batch
Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries
NEW QUESTION # 36
A SQL Dashboard was built for the supply chain team to monitor the inventory and product orders, but all of the timestamps displayed on the dashboards are showing in UTC format, so they requested to change the time zone to the location of New York. How would you approach resolving this issue?
- A. Add SET Timezone = America/New_York on every of the SQL queries in the dashboard.
- B. Change the timestamp on the delta tables to America/New_York format
- C. Change the spark configuration of SQL endpoint to format the timestamp to Ameri-ca/New_York
- D. Under SQL Admin Console, set the SQL configuration parameter time zone to Ameri-ca/New_York
- E. Move the workspace from Central US zone to East US Zone
Answer: D
Explanation:
Explanation
The answer is, Under SQL Admin Console, set the SQL configuration parameter time zone to America/New_York Here are steps you can take this to configure, so the entire dashboard is changed without changing individual queries Configure SQL parameters To configure all warehouses with SQL parameters:
1.Click Settings at the bottom of the sidebar and select SQL Admin Console.
2.Click the SQL Warehouse Settings tab.
3.In the SQL Configuration Parameters textbox, specify one key-value pair per line. Sepa-rate the name of the parameter from its value using a space. For example, to ena-ble ANSI_MODE:
Graphical user interface, text, application Description automatically generated
Similarly, we can add a line in the SQL Configuration parameters
timezone America/New_York
SQL configuration parameters | Databricks on AWS
NEW QUESTION # 37
Which of the following is the correct statement for a session scoped temporary view?
- A. Temporary views stored in memory
- B. Temporary views are lost once the notebook is detached and re-attached
- C. Temporary views can be still accessed even if cluster is restarted
- D. Temporary views are created in local_temp database
- E. Temporary views can be still accessed even if the notebook is detached and attached
Answer: B
Explanation:
Explanation
The answer is Temporary views are lost once the notebook is detached and attached There are two types of temporary views that can be created, Session scoped and Global
*A local/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 reattached local 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 # 38
......
Updated Databricks-Certified-Professional-Data-Engineer Dumps Questions For Databricks Exam: https://www.updatedumps.com/Databricks/Databricks-Certified-Professional-Data-Engineer-updated-exam-dumps.html