[2024] Databricks-Certified-Professional-Data-Engineer Answers Databricks-Certified-Professional-Data-Engineer Free Demo Are Based On The Real Exam [Q12-Q37]

[2024] Databricks-Certified-Professional-Data-Engineer Answers Databricks-Certified-Professional-Data-Engineer Free Demo Are Based On The Real Exam

Databricks-Certified-Professional-Data-Engineer [Oct-2024 Newly Released] Exam Questions For You To Pass

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who work with Databricks. It demonstrates that the candidate has a deep understanding of Databricks and can use it effectively to solve complex data engineering problems. Databricks Certified Professional Data Engineer Exam certification can help data engineers advance their careers, increase their earning potential, and gain recognition as experts in the field of big data and machine learning.

Databricks Certified Professional Data Engineer certification is globally recognized by various industries, including finance, healthcare, government, and technology. Databricks Certified Professional Data Engineer Exam certification validates the candidate's knowledge level in data engineering solutions and qualifies them to work with Databricks's technology. The certified professional can optimize and manage their organization's data in the cloud using Databricks, which results in timely and informed decisions.

NEW QUESTION # 12
Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?

A. You can not create a view on streaming data source.
B. Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("streaming_vw")
C. Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
D. Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")
E. Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
F. Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("streaming_vw")

Answer: C

Explanation:
Explanation
The answer is
Spark.readStream.table("sales").createOrReplaceTempView("streaming_vw") When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started.
You can load both paths and tables as a stream, you also have the ability to ignore deletes and changes(updates, Merge, overwrites) on the delta table.
Here is more information,
https://docs.databricks.com/delta/delta-streaming.html#delta-table-as-a-source

NEW QUESTION # 13
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?

A. 1.(spark.table("sales").agg(sum("sales"),sum("units"))
2..writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
B. 1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
C. 1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") )
D. 1.(spark.table("sales").groupBy("store")
2..agg(sum("sales")).writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
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: E

Explanation:
Explanation
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 14
A table customerLocations exists with the following schema:
1. id STRING,
2. date STRING,
3. city STRING,
4. country STRING
A senior data engineer wants to create a new table from this table using the following command:
1. CREATE TABLE customersPerCountry AS
2. SELECT country,
3. COUNT(*) AS customers
4. FROM customerLocations
5. GROUP BY country;
A junior data engineer asks why the schema is not being declared for the new table. Which of the following
responses explains why declaring the schema is not necessary?

A. CREATE TABLE AS SELECT statements infer the schema by scanning the data
B. CREATE TABLE AS SELECT statements adopt schema details from the source table and query
C. CREATE TABLE AS SELECT statements result in tables where schemas are optional
D. CREATE TABLE AS SELECT statements result in tables that do not support schemas
E. CREATE TABLE AS SELECT statements assign all columns the type STRING

Answer: B

NEW QUESTION # 15
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
C. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
E. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

Answer: A

NEW QUESTION # 16
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?

A. Triggered, Continuous
B. Once, Continuous
C. Continuous, Incremental
D. Triggered, Incremental
E. Once, Incremental

Answer: A

Explanation:
Explanation
The answer is Triggered, Continuous
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-concepts#-
*Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipeline are updated after their dependent data sources have been updated.
*Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data.

NEW QUESTION # 17
A data engineer is 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. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater than 60.
B. They can set up an Alert for the query to notify when the ELT job fails.
C. This type of alert is not possible in Databricks
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 accompanying dashboard to notify when it has not re-freshed in 60 minutes.

Answer: D

Explanation:
Explanation
The answer is, They can set up an Alert for the query to notify them if the returned value is greater than 60.
The important thing to note here is that alert can only be setup on query not on the dashboard, query can return a value, which is used if alert can be triggered.

NEW QUESTION # 18
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. 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.
B. 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.
C. 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.
D. 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.
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 # 19
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. All records are cached to an operational database and then the filter is applied
B. The Parquet file footers are scanned for min and max statistics for the latitude column
C. All records are cached to attached storage and then the filter is applied
D. The Hive metastore is scanned for min and max statistics for the latitude column
E. The Delta log is scanned for min and max statistics for the latitude column

Answer: E

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 # 20
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?

A. Delta time travel
B. The stream will failover to available nodes in the cluster
C. Checkpointing and write-ahead logging
D. Write ahead logging and watermarking
E. Checkpointing and Watermarking
F. Checkpointing and Idempotent sinks

Answer: C

Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.

NEW QUESTION # 21
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

Answer: B

Explanation:
Explanation
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 22
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. Define units test and functions within the same notebook
B. Define and unit test functions using Files in Repos
C. Run unit tests against non-production data that closely mirrors production
D. Define and import unit test functions from a separate Databricks notebook

Answer: C

NEW QUESTION # 23
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Executor's detail screen and Executor's log files
B. Stage's detail screen and Executor's files
C. Stage's detail screen and Query's detail screen
D. Driver's and Executor's log files

Answer: C

Explanation:
In Apache Spark's UI, indicators of data spilling to disk during the execution of wide transformations can be found in the Stage's detail screen and the Query's detail screen. These screens provide detailed metrics about each stage of a Spark job, including information about memory usage and spill data. If a task is spilling data to disk, it indicates that the data being processed exceeds the available memory, causing Spark to spill data to disk to free up memory. This is an important performance metric as excessive spill can significantly slow down the processing.
References:
* Apache Spark Monitoring and Instrumentation: Spark Monitoring Guide
* Spark UI Explained: Spark UI Documentation

NEW QUESTION # 24
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
B. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
C. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
E. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

Answer: B

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 25
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

A. The tables should be refreshed in the writing cluster before the next query is run
B. The tables should be altered to include metadata to not cache
C. The tables should be updated before the next query is run
D. The tables should be stored in a cloud-based external system
E. The tables should be converted to the Delta format

Answer: E

NEW QUESTION # 26
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

A. f"{region}{store}_sales_2024"
B. "{region}{store}_sales_2024"
C. "{region}+{store}+_sales_+2024"
D. f"{region}+{store}+_sales_+2024"
E. "{region}+{store}+"_sales_"+2024"

Answer: A

NEW QUESTION # 27
A table named user_ltv is 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.
The user_ltv table 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. The email and ltv columns will be returned with the values in user itv.
B. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
C. Only the email and itv columns will be returned; the email column will contain all null values.
D. The email, age. and ltv columns will be returned with the values in user ltv.
E. Three columns will be returned, but one column will be named "redacted" and contain only null values.

Answer: B

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 # 28
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
B. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
D. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Answer: A

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 29
The business intelligence team has a dashboard configured to track various summary metrics for retail stories.
This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
B. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C. Define a view against the products_per_order table and define the dashboard against this view.
D. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

Answer: A

Explanation:
Given the requirement for daily refresh of data and the need to ensure quick response times for interactive queries while controlling costs, a nightly batch job to pre-compute and save the required summary metrics is the most suitable approach.
* By pre-aggregating data during off-peak hours, the dashboard can serve queries quickly without requiring on-the-fly computation, which can be resource-intensive and slow, especially with many users.
* This approach also limits the cost by avoiding continuous computation throughout the day and instead leverages a batch process that efficiently computes and stores the necessary data.
* The other options (A, C, D) either do not address the cost and performance requirements effectively or are not suitable for the use case of less frequent data refresh and high interactivity.
References:
* Databricks Documentation on Batch Processing: Databricks Batch Processing
* Data Lakehouse Patterns: Data Lakehouse Best Practices

NEW QUESTION # 30
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. The connection to the external table will succeed; the string value of password will be printed in plain text.
B. 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.
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:
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 # 31
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A.
B.
C. preds.write.mode("append").saveAsTable("churn_preds")
D. preds.write.format("delta").save("/preds/churn_preds")
E.

Answer: C

NEW QUESTION # 32
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 increase the maximum bound of the SQL endpoint(SQL warehouse)'s scaling range
B. They can turn on the Serverless feature for the SQL endpoint(SQL warehouse) and change the Spot Instance Policy to "Reliability Optimized."
C. They can turn on the Auto Stop feature for the SQL endpoint(SQL warehouse).
D. They can increase the warehouse size from 2X-Smal to 4XLarge of the SQL end-point(SQL warehouse).
E. They can turn on the Serverless feature for the SQL endpoint(SQL warehouse).

Answer: A

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 # 33
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Answer: C

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.
References:
* Databricks Documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 34
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Perform a full outer join on a unique key and overwrite existing data.
B. Perform an insert-only merge with a matching condition on a unique key.
C. Set the configuration delta.deduplicate = true.
D. Rely on Delta Lake schema enforcement to prevent duplicate records.
E. VACUUM the Delta table after each batch completes.

Answer: B

Explanation:
Explanation
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.
References:
https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge
https://docs.databricks.com/delta/delta-update.html#insert-only-merge

NEW QUESTION # 35
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
B. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
D. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

Answer: E

Explanation:
Explanation
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 36
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Configure a job that executes every time new data lands in a given directory.
B. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D. Schedule a job to execute the pipeline once hour on a new job cluster.

Answer: D

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
Reference:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html

NEW QUESTION # 37
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Databricks Certified Professional Data Engineer exam consists of multiple-choice questions and is conducted online. Databricks-Certified-Professional-Data-Engineer exam is intended to measure the candidate's proficiency in various areas, such as Spark architecture, Spark programming, data processing, data analysis, and data modeling. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize Spark performance and troubleshoot Spark applications. It is recommended that individuals who plan to take Databricks-Certified-Professional-Data-Engineer exam have at least two years of hands-on experience in big data technologies and Apache Spark.

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