2024 Updated Databricks Databricks-Certified-Professional-Data-Engineer Dumps PDF - Want To Pass Databricks-Certified-Professional-Data-Engineer Fast [Q11-Q36]

2024 Updated Databricks Databricks-Certified-Professional-Data-Engineer Dumps PDF - Want To Pass Databricks-Certified-Professional-Data-Engineer Fast

Databricks-Certified-Professional-Data-Engineer Practice Exam Dumps - 99% Marks In Databricks Exam

Databricks is a leading company in the field of data engineering, providing a cloud-based platform for collaborative data analysis and processing. The company's platform is used by a wide range of companies and organizations, including Fortune 500 companies, government agencies, and academic institutions. Databricks offers a range of certifications to help professionals demonstrate their proficiency in using the platform, including the Databricks Certified Professional Data Engineer certification.

NEW QUESTION # 11
You are working on a table called orders which contains data for 2021 and you have the second table called orders_archive which contains data for 2020, you need to combine the data from two tables and there could be a possibility of the same rows between both the tables, you are looking to combine the results from both the tables and eliminate the duplicate rows, which of the following SQL statements helps you accomplish this?

A. SELECT * FROM orders_archive MINUS SELECT * FROM orders
B. SELECT * FROM orders UNION ALL SELECT * FROM orders_archive
C. SELECT distinct * FROM orders JOIN orders_archive on order.id = or-ders_archive.id
D. SELECT * FROM orders UNION SELECT * FROM orders_archive
(Correct)
E. SELECT * FROM orders INTERSECT SELECT * FROM orders_archive

Answer: D

Explanation:
Explanation
Answer is SELECT * FROM orders UNION SELECT * FROM orders_archive
UNION and UNION ALL are set operators,
UNION combines the output from both queries but also eliminates the duplicates.
UNION ALL combines the output from both queries.

NEW QUESTION # 12
You had AUTO LOADER to process millions of files a day and noticed slowness in load process, so you scaled up the Databricks cluster but realized the performance of the Auto loader is still not improving, what is the best way to resolve this.

A. Merge files to one large file
B. Copy the data from cloud storage to local disk on the cluster for faster access
C. Increase the maxFilesPerTrigger option to a sufficiently high number
D. Setup a second AUTO LOADER process to process the data
E. AUTO LOADER is not suitable to process millions of files a day

Answer: C

Explanation:
Explanation
The default value of maxFilesPerTrigger is 1000 it can be increased to a much higher number but will require a much larger compute to process.
Graphical user interface, text, application, email Description automatically generated

https://docs.databricks.com/ingestion/auto-loader/options.html

NEW QUESTION # 13
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day.
At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
C. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
E. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.

Answer: D

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time1. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job2.
The other options are not correct, because:
* Option A is incorrect because triggering batches more frequently does not allow idle executors to begin processing the next batch while longer running tasks from previous batches finish. In fact, the opposite is true. Triggering batches more frequently may cause concurrent batches to compete for the same resources and cause contention and backpressure2. This can degrade the performance and stability of the streaming job.
* Option B is incorrect because increasing the trigger interval to 30 seconds is not a good practice to ensure no records are dropped. Increasing the trigger interval means that the streaming query will process larger batches of data less frequently, which can increase the risk of spill, memory pressure, and timeouts12. This can also increase the latency and reduce the throughput of the streaming job.
* Option C is incorrect because the trigger interval can be modified without modifying the checkpoint directory. The checkpoint directory stores the metadata and state of the streaming query, such as the offsets, schema, and configuration3. Changing the trigger interval does not affect the state of the streaming query, and does not require a new checkpoint directory. However, changing the number of shuffle partitions may affect the state of the streaming query, and may require a new checkpoint directory4.
* Option D is incorrect because using the trigger once option and configuring a Databricks job to execute the query every 10 seconds does not ensure that all backlogged records are processed with each batch. The trigger once option means that the streaming querywill process all the available data in the source and then stop5. However, this does not guarantee that the query will finish processing within 10 seconds, especially if there are a lot of records in the source. Moreover, configuring a Databricks job to execute the query every 10 seconds may cause overlapping or missed batches, depending on the execution time of the query.
References: Memory Management Overview, Structured Streaming Performance Tuning Guide, Checkpointing, Recovery Semantics after Changes in a Streaming Query, Triggers

NEW QUESTION # 14
Kevin is the owner of both the sales table and regional_sales_vw view which uses the sales table as the underlying source for the data, and Kevin is looking to grant select privilege on the view regional_sales_vw to one of newly joined team members Steven. Which of the following is a true statement?

A. Kevin although is the owner but does not have ALL PRIVILEGES permission
B. Steve will also require SELECT access on the underlying table
C. Kevin can grant access to the view, because he is the owner of the view and the under-lying table
D. Kevin can not grant access to Steven since he does not have security admin privilege
E. Kevin can not grant access to Steven since he does have workspace admin privilege

Answer: C

Explanation:
Explanation
The answer is, Kevin can grant access to the view, because he is the owner of the view and the un-derlying table, Ownership determines whether or not you can grant privileges on derived objects to other users, a user who creates a schema, table, view, or function becomes its owner. The owner is granted all privileges and can grant privileges to other users

NEW QUESTION # 15
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. return spark.readStream.load("bronze")
D.
E.

Answer: E

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 16
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. Change the spark configuration of SQL endpoint to format the timestamp to Ameri-ca/New_York
B. Under SQL Admin Console, set the SQL configuration parameter time zone to Ameri-ca/New_York
C. Move the workspace from Central US zone to East US Zone
D. Change the timestamp on the delta tables to America/New_York format
E. Add SET Timezone = America/New_York on every of the SQL queries in the dashboard.

Answer: B

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 # 17
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-output
B. /jobs/runs/get
C. /jobs/runs/list
D. /jobs/list
E. /jobs/get

Answer: E

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 # 18
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 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
B. 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
C. 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
D. Stop the existing pipeline; use the returned settings in a reset command

Answer: C

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 # 19
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Only the email and itv columns will be returned; the email column will contain all null values.
B. Three columns will be returned, but one column will be named "redacted" and contain only null values.
C. The email, age. and ltv columns will be returned with the values in user ltv.
D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E. The email and ltv columns will be returned with the values in user itv.

Answer: D

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 20
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?

A. The SQL endpoint being used by each of the queries might need a few minutes to start up
B. The queries attached to the dashboard might take a few minutes to run under normal circumstances
C. The Job associated with updating the dashboard might be using a non-pooled endpoint
D. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters
E. The queries attached to the dashboard might first be checking to determine if new data is available

Answer: C

NEW QUESTION # 21
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the fieldpk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
E. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

Answer: B

Explanation:
Explanation
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will alwaysreflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 22
What are the different ways you can schedule a job in Databricks workspace?

A. On-Demand runs, File notification from Cloud object storage
B. Cron, On Demand runs
C. Cron, File notification from Cloud object storage
D. Once, Continuous
E. Continuous, Incremental

Answer: B

Explanation:
Explanation
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax

NEW QUESTION # 23
Which distribution does Databricks support for installing custom Python code packages?

A. CRAN
B. CRAM
C. Wheels
D. nom
E. jars
F. sbt

Answer: D

NEW QUESTION # 24
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFramedf. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFramedfhas 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. lag("event_time", "10 minutes").alias("time")
B. to_interval("event_time", "5 minutes").alias("time")
C. window("event_time", "5 minutes").alias("time")
D. window("event_time", "10 minutes").alias("time")
E. "event_time"

Answer: C

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "WINDOW" section.https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-struc

NEW QUESTION # 25
What statement is true regarding the retention of job run history?

A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
B. t is retained for 60 days, during which you can export notebook run results to HTML
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, after which logs are archived
E. It is retained until you export or delete job run logs

Answer: B

NEW QUESTION # 26
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
B. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
C. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
E. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

Answer: E

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.

NEW QUESTION # 27
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

A. SQL Endpoints
B. JOBS and TASKS
C. AUTO LOADER
D. DELTA LIVE TABLES
E. STRUCTURED STREAMING with MULTI HOP

Answer: D

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious

NEW QUESTION # 28
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Read
B. Can Manage
C. Can Run
D. Can Edit
E. No permissions

Answer: E

Explanation:
This is the correct answer because it is the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data. Notebook permissions are used to control access to notebooks in Databricks workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can Run, and Can Read. Can Manage allows full control over the notebook, including editing, running, deleting, exporting, and changing permissions. Can Edit allows modifying and running the notebook, but not changing permissions or deleting it. Can Run allows executing commands in an existing cluster attached to the notebook, but not modifying or exporting it. Can Read allows viewing the notebook content, but not running or modifying it. In this case, granting Can Read permission to the user will allow them to review the production logic in the notebook without allowing them to make any changes to it or run any commands that may affect production data. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Notebook permissions" section.

NEW QUESTION # 29
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Delta log is scanned for min and max statistics for the latitude column
B. The Hive metastore is scanned for min and max statistics for the latitude column
C. The Parquet file footers are scanned for min and max statistics for the latitude column
D. All records are cached to an operational database and then the filter is applied
E. All records are cached to attached storage and then the filter is applied

Answer: A

Explanation:
Explanation
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column

NEW QUESTION # 30
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. 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.
B. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
D. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E. 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.

Answer: A

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 # 31
The data analyst team had put together queries that identify items that are out of stock based on orders and replenishment but when they run all together for final output the team noticed it takes a really long time, you were asked to look at the reason why queries are running slow and identify steps to improve the performance and when you looked at it you noticed all the code queries are running sequentially and using a SQL endpoint cluster. Which of the following steps can be taken to resolve the issue?
Here is the example query
1.--- Get order summary
2.create or replace table orders_summary
3.as
4.select product_id, sum(order_count) order_count
5.from
6. (
7. select product_id,order_count from orders_instore
8. union all
9. select product_id,order_count from orders_online
10. )
11.group by product_id
12.-- get supply summary
13.create or repalce tabe supply_summary
14.as
15.select product_id, sum(supply_count) supply_count
16.from supply
17.group by product_id
18.
19.-- get on hand based on orders summary and supply summary
20.
21.with stock_cte
22.as (
23.select nvl(s.product_id,o.product_id) as product_id,
24. nvl(supply_count,0) - nvl(order_count,0) as on_hand
25.from supply_summary s
26.full outer join orders_summary o
27. on s.product_id = o.product_id
28.)
29.select *
30.from
31.stock_cte
32.where on_hand = 0

A. Turn on the Serverless feature for the SQL endpoint and change the Spot Instance Pol-icy to "Reliability Optimized."
B. Turn on the Auto Stop feature for the SQL endpoint.
C. Turn on the Serverless feature for the SQL endpoint.
D. Increase the cluster size of the SQL endpoint.
E. Increase the maximum bound of the SQL endpoint's scaling range.

Answer: D

Explanation:
Explanation
The answer is to increase the cluster size of the SQL Endpoint, here queries are running sequentially and since the single query can not span more than one cluster adding more clusters won't improve the query but rather increasing the cluster size will improve performance so it can use additional compute in a warehouse.
In the exam please note that additional context will not be given instead you have to look for cue words or need to understand if the queries are running sequentially or concurrently. if the que-ries are running sequentially then scale up(more nodes) if the queries are running concurrently (more users) then scale out(more clusters).
Below is the snippet from Azure, as you can see by increasing the cluster size you are able to add more worker nodes.

SQL endpoint scales horizontally(scale-out) and vertically (scale-up), you have to understand when to use what.
Scale-up-> Increase the size of the cluster from x-small to small, to medium, X Large....
If you are trying to improve the performance of a single query having additional memory, additional nodes and cpu in the cluster will improve the performance.
Scale-out -> Add more clusters, 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.
SQL endpoint
A picture containing diagram Description automatically generated

NEW QUESTION # 32
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
B. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
D. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
E. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Answer: C

Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

NEW QUESTION # 33
Which of the following scenarios is the best fit for AUTO LOADER?

A. Efficiently move data incrementally from one delta table to another delta table
B. Efficiently process new data incrementally from cloud object storage
C. Incrementally process new data from streaming data sources like Kafka into delta lake
D. Incrementally process new data from relational databases like MySQL
E. Efficiently copy data from one data lake location to another data lake location

Answer: B

Explanation:
Explanation
The answer is, Efficiently process new data incrementally from cloud object storage, AU-TO LOADER only supports ingesting files stored in a cloud object storage. Auto Loader cannot process streaming data sources like Kafka or Delta streams, use Structured streaming for these data sources.
Diagram Description automatically generated

Auto Loader and Cloud Storage Integration
Auto Loader supports a couple of ways to ingest data incrementally
1.Directory listing - List Directory and maintain the state in RocksDB, supports incremental file listing
2.File notification - Uses a trigger+queue to store the file notification which can be later used to retrieve the file, unlike Directory listing File notification can scale up to millions of files per day.
[OPTIONAL]
Auto Loader vs COPY INTO?
Auto Loader
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a new Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
When to use Auto Loader instead of the COPY INTO?
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*You do not plan to load subsets of previously uploaded files. With Auto Loader, it can be more difficult to reprocess subsets of files. However, you can use the COPY INTO SQL command to reload subsets of files while an Auto Loader stream is simultaneously running.

NEW QUESTION # 34
A newly joined team member John Smith in the Marketing team currently has access read access to sales tables but does not have access to update the table, which of the following commands help you accomplish this?

A. GRANT MODIFY ON TABLE table_name TO [email protected]
B. GRANT USAGE ON TABLE table_name TO [email protected]
C. GRANT UPDATE ON TABLE table_name TO [email protected]
D. GRANT UPDATE TO TABLE table_name ON [email protected]
E. GRANT MODIFY TO TABLE table_name ON [email protected]

Answer: A

Explanation:
Explanation
The answer is GRANT MODIFY ON TABLE table_name TO [email protected]
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#privileges

NEW QUESTION # 35
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day.
At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
C. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
E. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.

Answer: D

Explanation:
Explanation
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time1. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job2.
The other options are not correct, because:
Option A is incorrect because triggering batches more frequently does not allow idle executors to begin processing the next batch while longer running tasks from previous batches finish. In fact, the opposite is true. Triggering batches more frequently may cause concurrent batches to compete for the same resources and cause contention and backpressure2. This can degrade the performance and stability of the streaming job.
Option B is incorrect because increasing the trigger interval to 30 seconds is not a good practice to ensure no records are dropped. Increasing the trigger interval means that the streaming query will process larger batches of data less frequently, which can increase the risk of spill, memory pressure, and timeouts12. This can also increase the latency and reduce the throughput of the streaming job.
Option C is incorrect because the trigger interval can be modified without modifying the checkpoint directory. The checkpoint directory stores the metadata and state of the streaming query, such as the offsets, schema, and configuration3. Changing the trigger interval does not affect the state of the streaming query, and does not require a new checkpoint directory. However, changing the number of shuffle partitions may affect the state of the streaming query, and may require a new checkpoint directory4.
Option D is incorrect because using the trigger once option and configuring a Databricks job to execute the query every 10 seconds does not ensure that all backlogged records are processed with each batch. The trigger once option means that the streaming query will process all the available data in the source and then stop5. However, this does not guarantee that the query will finish processing within 10 seconds, especially if there area lot of records in the source. Moreover, configuring a Databricks job to execute the query every 10 seconds may cause overlapping or missed batches, depending on the execution time of the query.
References: Memory Management Overview, Structured Streaming Performance Tuning Guide, Checkpointing, Recovery Semantics after Changes in a Streaming Query, Triggers

NEW QUESTION # 36
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