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NEW QUESTION # 30
Identify one of the below statements that can query a delta table in PySpark Dataframe API
- A. Spark.read.format("delta").LoadTableAs("table_name")
- B. Spark.read.mode("delta").table("table_name")
- C. Spark.read.format("delta").TableAs("table_name")
- D. Spark.read.table("table_name")
- E. Spark.read.table.delta("table_name")
Answer: D
NEW QUESTION # 31
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?
- A. /jobs/runs/get
- B. /jobs/runs/list
- C. /jobs/runs/get-output
- D. /jobs/get
- E. /jobs/list
Answer: D
Explanation:
Explanation
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.
NEW QUESTION # 32
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
- A. return spark.readStream.table("bronze")
- B. return spark.read.option("readChangeFeed", "true").table ("bronze")
- C.
- D. return spark.readStream.load("bronze")
Answer: C
Explanation:
Explanation
This is the correct answer because it completes the function definition that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline. The object returned by this function is a DataFrame that contains all change events from a Delta Lake table that has enabled change data feed. The readChangeFeed option is set to true to indicate that the DataFrame should read changes from the table, and the table argument specifies the name of the table to read changes from. The DataFrame will have a schema that includes four columns: operation, partition, value, and timestamp. The operation column indicates the type of change event, such as insert, update, or delete. The partition column indicates the partition where the change event occurred. The value column contains the actual data of the change event as a struct type. The timestamp column indicates the time when the change event was committed. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Read changes in batch queries" section.
NEW QUESTION # 33
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).
Which statement describes what will happen when the above code is executed?
- A. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
- B. The connection to the external table will succeed; the string value of password will be printed in plain text.
- C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
- D. The connection to the external table will fail; the string "redacted" will be printed.
- E. The connection to the external table will succeed; the string "redacted" will be printed.
Answer: E
Explanation:
Explanation
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.
NEW QUESTION # 34
Your team has hundreds of jobs running but it is difficult to track cost of each job run, you are asked to provide a recommendation on how to monitor and track cost across various workloads
- A. Use workspace admin reporting
- B. Use job logs to monitor and track the costs
- C. Use Tags, during job creation so cost can be easily tracked
- D. Use a single cluster for all the jobs, so cost can be easily tracked
- E. Create jobs in different workspaces, so we can track the cost easily
Answer: C
Explanation:
Explanation
The answer is Use Tags, during job creation so cost can be easily tracked Review below link for more details
https://docs.databricks.com/administration-guide/account-settings/usage-detail-tags-aws.html Here is a view how tags get propagated from pools to clusters and clusters without pools, Diagram Description automatically generated
NEW QUESTION # 35
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 increase the cluster size anywhere from X small to 3XL to review the per-formance and select the size that meets the required SLA.
- B. They can turn off the Auto Stop feature for the SQL endpoint to more than 30 mins.
- C. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy from
"Cost optimized" to "Reliability Optimized." - D. 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.
- E. They can turn on the Serverless feature for the SQL endpoint.
Answer: A
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 # 36
You noticed a colleague is manually copying the data to the backup folder prior to running an up-date command, incase if the update command did not provide the expected outcome so he can use the backup copy to replace table, which Delta Lake feature would you recommend simplifying the process?
- A. Use time travel feature to refer old data instead of manually copying
- B. Use SHADOW copy of the table as preferred backup choice
- C. Cloud object storage automatically backups the data
- D. Cloud object storage retains previous version of the file
- E. Use DEEP CLONE to clone the table prior to update to make a backup copy
Answer: A
Explanation:
Explanation
The answer is, Use time travel feature to refer old data instead of manually copying.
https://databricks.com/blog/2019/02/04/introducing-delta-time-travel-for-large-scale-data-lakes.html
1.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01"
2.SELECT count(*) FROM my_table TIMESTAMP AS OF date_sub(current_date(), 1)
3.SELECT count(*) FROM my_table TIMESTAMP AS OF "2019-01-01 01:30:00.000"
NEW QUESTION # 37
A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job's most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?
- A. This type of alerting is not possible in Databricks
- B. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60 - C. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes - D. They can set up an Alert for the query to notify them if the returned value is greater than 60
- E. They can set up an Alert for the query to notify when the ELT job fails
Answer: D
NEW QUESTION # 38
You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.
- A. Databricks notebooks support automatic change tracking and versioning
- B. Databricks notebooks should be copied to a local machine and setup source control lo-cally to version the notebooks
- C. Databricks notebooks can be exported into dbc archive files and stored in data lake
- D. Databricks notebook can be exported as HTML and imported at a later time
- E. Databricks Notebooks support real-time coauthoring on a single notebook
Answer: E
Explanation:
Explanation
Answer is Databricks Notebooks support real-time coauthoring on a single notebook Every change is saved, and a notebook can be changed my multiple users.
NEW QUESTION # 39
Which of the following is the correct statement for a session scoped temporary view?
- A. Temporary views are created in local_temp database
- B. Temporary views can be still accessed even if the notebook is detached and attached
- C. Temporary views can be still accessed even if cluster is restarted
- D. Temporary views stored in memory
- E. Temporary views are lost once the notebook is detached and re-attached
Answer: E
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 # 40
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
- A. return spark.readStream.table("bronze")
- B. return spark.read.option("readChangeFeed", "true").table ("bronze")
- C.
- D. return spark.readStream.load("bronze")
- E.
Answer: E
Explanation:
Explanation
https://docs.databricks.com/en/delta/delta-change-data-feed.html
NEW QUESTION # 41
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
- A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
- B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
- C. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
- D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
Answer: D
Explanation:
Explanation
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.
NEW QUESTION # 42
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?
- A. Run unit tests against non-production data that closely mirrors production
- B. Define units test and functions within the same notebook
- C. Define and unit test functions using Files in Repos
- D. Define and import unit test functions from a separate Databricks notebook
Answer: A
Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
References:
* Databricks Documentation on Testing: Testing and Validation of Data and Notebooks
NEW QUESTION # 43
Which of the following data workloads will utilize a Silver table as its source?
- A. A job that aggregates cleaned data to create standard summary statistics
- B. A job that queries aggregated data that already feeds into a dashboard
- C. A job that enriches data by parsing its timestamps into a human-readable format
- D. A job that cleans data by removing malformatted records
- E. A job that ingests raw data from a streaming source into the Lakehouse
Answer: A
NEW QUESTION # 44
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
- A. Spill resulting from attached volume storage being too small.
- B. Credential validation errors while pulling data from an external system.
- C. Task queueing resulting from improper thread pool assignment.
- D. Network latency due to some cluster nodes being in different regions from the source data
- E. Skew caused by more data being assigned to a subset of spark-partitions.
Answer: E
Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.
NEW QUESTION # 45
Which statement describes integration testing?
- A. Validates interactions between subsystems of your application
- B. Requires manual intervention
- C. Validates an application use case
- D. Requires an automated testing framework
- E. Validates behavior of individual elements of your application
Answer: A
Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.
NEW QUESTION # 46
Data science team has requested they are missing a column in the table called average price, this can be calculated using units sold and sales amt, which of the following SQL statements allow you to reload the data with additional column
- A. COPY INTO SALES AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
- B. 1.CREATE OR REPLACE TABLE sales
2.AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales - C. 1.INSERT OVERWRITE sales
2.SELECT *, salesAmt/unitsSold as avgPrice FROM sales - D. OVERWRITE sales AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
- E. MERGE INTO sales USING (SELECT *, salesAmt/unitsSold as avgPrice FROM sales)
Answer: B
Explanation:
Explanation
1.CREATE OR REPLACE TABLE sales
2.AS SELECT *, salesAmt/unitsSold as avgPrice FROM sales
The main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to overwrite schema, only when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command will fail.
NEW QUESTION # 47
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?
- A. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
- B. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
- C. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
- D. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
- E. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
Answer: C
Explanation:
Explanation
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html
NEW QUESTION # 48
Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql("select count(*) from table").collect()[0][0]
- A. assert row_count == 10, "Row count did not match"
- B. assert row_count = 10, "Row count did not match"
- C. assert if row_count == 10, "Row count did not match"
- D. assert (row_count = 10, "Row count did not match")
- E. assert if (row_count = 10, "Row count did not match")
Answer: A
Explanation:
Explanation
The answer is assert row_count == 10, "Row count did not match"
Review below documentation
NEW QUESTION # 49
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
- A. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
- B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
- C. Stop the existing pipeline; use the returned settings in a reset command
- D. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
Answer: D
Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use thedatabricks pipelines get --pipeline-idcommand to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing thepipeline_idto prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines createcommand to create a new pipeline with those settings.
References:
* Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines
NEW QUESTION # 50
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?
- A. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
- B. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
- C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
- D. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
- E. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
Answer: E
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum
NEW QUESTION # 51
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?
- A. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better perfor-mance.
- B. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
- C. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.
- D. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
- E. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused.
(Correct)
Answer: E
Explanation:
Explanation
VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data reten-tion for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can improve the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.
NEW QUESTION # 52
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?
- A. All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated - B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing - C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing - D. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline - E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped
Answer: C
NEW QUESTION # 53
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. Whenever a table is being created, make sure that the location keyword is used.
- C. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
- D. Whenever a database is being created, make sure that the location keyword is used
- E. When tables are created, make sure that the external keyword is used in the create table statement.
Answer: B
NEW QUESTION # 54
Which of the following is a Continuous Probability Distributions?
- A. Negative binomial distribution
- B. Poisson probability distribution
- C. Binomial probability distribution
- D. Normal probability distribution
Answer: D
NEW QUESTION # 55
......
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