[Feb 24, 2023] 100% Pass Guarantee for Databricks-Certified-Professional-Data-Engineer Dumps with Actual Exam Questions
Today Updated Databricks-Certified-Professional-Data-Engineer Exam Dumps Actual Questions
NEW QUESTION 127
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 a single cluster for all the jobs, so cost can be easily tracked
- B. Use Tags, during job creation so cost can be easily tracked
- C. Create jobs in different workspaces, so we can track the cost easily
- D. Use job logs to monitor and track the costs
- E. Use workspace admin reporting
Answer: B
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 128
Which of the following SQL statements can be used to update a transactions table, to set a flag on the table from Y to N
- A. UPDATE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- B. MODIFY transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- C. REPLACE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
- D. MERGE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
Answer: C
Explanation:
Explanation
The answer is
UPDATE transactions SET active_flag = 'N' WHERE active_flag = 'Y'
Delta Lake supports UPDATE statements on the delta table, all of the changes as part of the update are ACID compliant.
NEW QUESTION 129
When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?
- A. Provide the schema of the source table in the cloudfiles.schemalocation
- B. Update the checkpoint location
- C. Provide schema hints
- D. Correct the incoming data by explicitly casting the data types
- E. Provide the schema of the target table in the cloudfiles.schemalocation
Answer: C
Explanation:
Explanation
The answer is, Provide schema hints.
1.spark.readStream \
2.format("cloudFiles") \
3.option("cloudFiles.format", "csv") \
4.option("header", "true") \
5.option("cloudFiles.schemaLocation", schema_location) \
6.option("cloudFiles.schemaHints", "id int, description string")
7.load(raw_data_location)
8.writeStream \
9.option("checkpointLocation", checkpoint_location) \
10.start(target_delta_table_location)option("cloudFiles.schemaHints", "id int, description string")
# Here we are providing a hint that id column is int and the description is a string When cloudfiles.schemalocation is used to store the output of the schema inference during the load process, with schema hints you can enforce data types for known columns ahead of time.
NEW QUESTION 130
What is the main difference between the below two commands?
1.INSERT OVERWRITE table_name
2.SELECT * FROM table
1.CREATE OR REPLACE TABLE table_name
2.AS SELECT * FROM table
- A. INSERT OVERWRITE replaces data by default, CREATE OR REPLACE replaces data and Schema by default
- B. INSERT OVERWRITE clears historical data versions by de-fault, CREATE OR REPLACE maintains the historical data versions by default
- C. INSERT OVERWRITE maintains historical data versions by de-fault, CREATE OR REPLACEclears the historical data versions by default
- D. INSERT OVERWRITE replaces data and schema by default, CREATE OR REPLACEreplaces data by default
- E. Both are same and results in identical outcomes
Answer: A
Explanation:
Explanation
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 131
The Delta Live Table Pipeline is configured to run in Production mode using the continuous Pipe-line Mode.
what is the expected outcome after clicking Start to update the pipeline?
- A. All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)
- B. 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
- C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated
- D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing
- E. 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
Answer: A
Explanation:
Explanation
The answer is,
All datasets will be updated continuously and the pipeline will not shut down. The compute re-sources will persist with the pipeline until it is shut down since the execution mode is chosen to be continuous. It does not matter if the pipeline mode is development or production, pipeline mode only matters during the pipeline initialization.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
Development:
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
Production:
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale cre-dentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default,
pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Delta Live Tables supports two different modes of execution:
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 pipe-line 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 Please review additional DLT concepts using the below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c
NEW QUESTION 132
What is the purpose of a silver layer in Multi hop architecture?
- A. Refined views with aggregated data
- B. Optimized query performance for business-critical data
- C. Replaces a traditional data lake
- D. A schema is enforced, with data quality checks.
- E. Efficient storage and querying of full and unprocessed history of data
Answer: D
Explanation:
Explanation
The answer is, A schema is enforced, with data quality checks.
Medallion Architecture - Databricks
Silver Layer:
1.Reduces data storage complexity, latency, and redundency
2.Optimizes ETL throughput and analytic query performance
3.Preserves grain of original data (without aggregation)
4.Eliminates duplicate records
5.production schema enforced
6.Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
NEW QUESTION 133
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 continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline - 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 be
terminated - D. 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 - E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing
Answer: E
NEW QUESTION 134
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. Continuous, Incremental
- D. Cron, File notification from Cloud object storage
- E. Once, Continuous
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 135
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?
- A. The stream will failover to available nodes in the cluster
- B. Checkpointing and write-ahead logging
- C. Delta time travel
- D. Checkpointing and Watermarking
- E. Write ahead logging and watermarking
- F. Checkpointing and Idempotent sinks
Answer: B
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 136
How does Lakehouse replace the dependency on using Data lakes and Data warehouses in a Data and Analytics solution?
- A. Supports BI and Machine learning workloads
- B. Open, direct access to data stored in standard data formats.
- C. Supports ACID transactions.
- D. Support for end-to-end streaming and batch workloads
- E. All the above
Answer: E
Explanation:
Explanation
Lakehouse combines the benefits of a data warehouse and data lakes,
Lakehouse = Data Lake + DataWarehouse
Here are some of the major benefits of a lakehouse
Text, letter Description automatically generated
Lakehouse = Data Lake + DataWarehouse
A picture containing text, blackboard Description automatically generated
NEW QUESTION 137
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. Under SQL Admin Console, set the SQL configuration parameter time zone to Ameri-ca/New_York
- B. Add SET Timezone = America/New_York on every of the SQL queries in the dashboard.
- C. Change the spark configuration of SQL endpoint to format the timestamp to Ameri-ca/New_York
- D. Change the timestamp on the delta tables to America/New_York format
- E. Move the workspace from Central US zone to East US Zone
Answer: A
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 138
What is the main difference between the silver layer and the gold layer in medalion architecture?
- A. God is a copy of silver data
- B. Silver is a copy of bronze data
- C. Silver may contain aggregated data
- D. Gold may contain aggregated data
- E. Data quality checks are applied in gold
Answer: D
Explanation:
Explanation
Medallion Architecture - Databricks
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence
NEW QUESTION 139
A junior data engineer needs to create a Spark SQL table my_table for which Spark manages both the data and
the metadata. The metadata and data should also be stored in the Databricks Filesystem (DBFS).
Which of the following commands should a senior data engineer share with the junior data engineer to
complete this task?
- A. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
- B. 1. CREATE TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path") - C. 1. CREATE TABLE my_table (id STRING, value STRING);
- D. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);
- E. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path");
Answer: C
NEW QUESTION 140
You are working on a dashboard that takes a long time to load in the browser, due to the fact that each visualization contains a lot of data to populate, which of the following approaches can be taken to address this issue?
- A. Increase size of the SQL endpoint cluster
- B. Use Databricks SQL Query filter to limit the amount of data in each visualization
- C. Increase the scale of maximum range of SQL endpoint cluster
- D. Use Delta cache to store the intermediate results
- E. Remove data from Delta Lake
Answer: B
Explanation:
Explanation
Note*: The question may sound misleading but these are types of questions the exam tries to ask.
A query filter lets you interactively reduce the amount of data shown in a visualization, similar to query parameter but with a few key differences. A query filter limits data after it has been loaded into your browser.
This makes filters ideal for smaller datasets and environments where query executions are time-consuming, rate-limited, or costly.
This query filter is different from than filter that needs to be applied at the data level, this filter is at the visualization level so you can toggle how much data you want to see.
1.SELECT action AS `action::filter`, COUNT(0) AS "actions count"
2.FROM events
3.GROUP BY action
When queries have filters you can also apply filters at the dashboard level. Select the Use Dash-board Level Filters checkbox to apply the filter to all queries.
Dashboard filters
Query filters | Databricks on AWS
NEW QUESTION 141
Which of the following python statement can be used to replace the schema name and table name in the query statement?
- A. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from { schema_name}.{table_name}" - B. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from + schema_name +"."+table_name" - C. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from schema_name.table_name" - D. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = "select * from {schema_name}.{table_name}"
Answer: A
Explanation:
Explanation
Answer is
table_name = "sales"
query = f"select * from {schema_name}.{table_name}"
f strings can be used to format a string. f" This is string {python variable}"
https://realpython.com/python-f-strings/
NEW QUESTION 142
You have configured AUTO LOADER to process incoming IOT data from cloud object storage every 15 mins, recently a change was made to the notebook code to update the processing logic but the team later realized that the notebook was failing for the last 24 hours, what steps team needs to take to reprocess the data that was not loaded after the notebook was corrected?
- A. Delete the checkpoint folder and run the autoloader again
- B. Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them
- C. Autoloader automatically re-processes data that was not loaded
- D. Manually re-load the data
- E. Enable back_fill = TRUE to reprocess the data
Answer: C
Explanation:
Explanation
The answer is,
Autoloader automatically re-processes data that was not loaded using the checkpoint.
NEW QUESTION 143
Data engineering team has a job currently setup to run a task load data into a reporting table every day at 8: 00 AM takes about 20 mins, Operations teams are planning to use that data to run a second job, so they access latest complete set of data. What is the best to way to orchestrate this job setup?
- A. Add Operation reporting task in the same job and set the Data Engineering task to de-pend on Operations reporting task
- B. Setup a Delta live to table based on the first table, set the job to run in continuous mode
- C. Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task
- D. Use Auto Loader to run every 20 mins to read the initial table and set the trigger to once and create a second job
- E. Setup a second job to run at 8:20 AM in the same workspace
Answer: C
Explanation:
Explanation
The answer is Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task.
Diagram Description automatically generated with medium confidence
NEW QUESTION 144
The operations team is interested in monitoring the recently launched product, team wants to set up an email alert when the number of units sold increases by more than 10,000 units. They want to monitor this every 5 mins.
Fill in the below blanks to finish the steps we need to take
* Create ___ query that calculates total units sold
* Setup ____ with query on trigger condition Units Sold > 10,000
* Setup ____ to run every 5 mins
* Add destination ______
- A. Python, Job, Refresh, email address
- B. SQL, Alert, Refresh, email address
- C. SQL, Job, Refresh, email address
- D. SQL, Job, SQL Cluster, email address
- E. Python, Job, SQL Cluster, email address
Answer: B
Explanation:
Explanation
The answer is SQL, Alert, Refresh, email address
Here the steps from Databricks documentation,
Create an alert
Follow these steps to create an alert on a single column of a query.
1.Do one of the following:
*Click Create in the sidebar and select Alert.
*Click Alerts in the sidebar and click the + New Alert button.
2.Search for a target query.
Graphical user interface, text, application Description automatically generated
To alert on multiple columns, you need to modify your query. See Alert on multiple col-umns.
3.In the Trigger when field, configure the alert.
*The Value column drop-down controls which field of your query result is evaluated.
*The Condition drop-down controls the logical operation to be applied.
*The Threshold text input is compared against the Value column using the Condition you specify.
Note
If a target query returns multiple records, Databricks SQL alerts act on the first one. As you change the Value column setting, the current value of that field in the top row is shown beneath it.
4.In the When triggered, send notification field, select how many notifications are sent when your alert is triggered:
*Just once: Send a notification when the alert status changes from OK to TRIGGERED.
*Each time alert is evaluated: Send a notification whenever the alert status is TRIGGERED regardless of its status at the previous evaluation.
*At most every: Send a notification whenever the alert status is TRIGGERED at a spe-cific interval. This choice lets you avoid notification spam for alerts that trigger of-ten.
Regardless of which notification setting you choose, you receive a notification whenever the status goes from OK to TRIGGERED or from TRIGGERED to OK. The schedule settings affect how many notifications you will receive if the status remains TRIGGERED from one execution to the next. For details, see Notification frequency.
5.In the Template drop-down, choose a template:
*Use default template: Alert notification is a message with links to the Alert configuration screen and the Query screen.
*Use custom template: Alert notification includes more specific information about the alert.
a.A box displays, consisting of input fields for subject and body. Any static content is valid, and you can incorporate built-in template variables:
*ALERT_STATUS: The evaluated alert status (string).
*ALERT_CONDITION: The alert condition operator (string).
*ALERT_THRESHOLD: The alert threshold (string or number).
*ALERT_NAME: The alert name (string).
*ALERT_URL: The alert page URL (string).
*QUERY_NAME: The associated query name (string).
*QUERY_URL: The associated query page URL (string).
*QUERY_RESULT_VALUE: The query result value (string or number).
*QUERY_RESULT_ROWS: The query result rows (value array).
*QUERY_RESULT_COLS: The query result columns (string array).
An example subject, for instance, could be: Alert "{{ALERT_NAME}}" changed status to
{{ALERT_STATUS}}.
b.Click the Preview toggle button to preview the rendered result.
Important
The preview is useful for verifying that template variables are rendered cor-rectly. It is not an accurate representation of the eventual notification content, as each alert destination can display notifications differently.
c.Click the Save Changes button.
6.In Refresh, set a refresh schedule. An alert's refresh schedule is independent of the query's refresh schedule.
*If the query is a Run as owner query, the query runs using the query owner's cre-dential on the alert's refresh schedule.
*If the query is a Run as viewer query, the query runs using the alert creator's cre-dential on the alert's refresh schedule.
7.Click Create Alert.
8.Choose an alert destination.
Important
If you skip this step you will not be notified when the alert is triggered.
NEW QUESTION 145
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?
- A. Change the spot instance policy from reliability optimized to cost optimized
- B. Always use X-small cluster
- C. Setup the dashboard refresh schedule to end in two weeks
- D. Reduce the max size of auto scaling from 10 to 5
- E. Reduce the size of the SQL Cluster size
Answer: C
Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks
NEW QUESTION 146
A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data
engineer suggests that this is inefficient and the table should simply be overwritten instead.
Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?
- A. Overwriting a table is an atomic operation and will not leave the table in an unfinished state
- B. Overwriting a table allows for concurrent queries to be completed while in progress
- C. Overwriting a table maintains the old version of the table for Time Travel
- D. Overwriting a table results in a clean table history for logging and audit purposes
- E. Overwriting a table is efficient because no files need to be deleted
Answer: D
NEW QUESTION 147
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?
- A. 1.(spark.table("sales").groupBy("store")
2..agg(sum("sales")).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.table("sales")
2..withColumn("avgPrice", col("sales") / col("units"))
3..writeStream
4..option("checkpointLocation", checkpointPath)
5..outputMode("append")
6..table("cleanedSales")) - D. 1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option("checkpointLocation", checkpointPath)
4..outputMode("append")
5..table("uncleanedSales") ) - E. 1.(spark.table("sales").agg(sum("sales"),sum("units"))
2..writeStream
3..option("checkpointLocation",checkpointPath)
4..outputMode("complete")
5..table("aggregatedSales"))
Answer: C
Explanation:
Explanation
A diagram of a house Description automatically generated with low confidence
NEW QUESTION 148
Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to
estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total
number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to
estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit
rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash
conclusions.
- A. Logistic Regression
- B. Naive Bayes
- C. Laplace Smoothing
- D. Linear Regression
Answer: C
Explanation:
Explanation
Smooth the estimates:consider flipping a coin for which the probability of heads is p, where p is unknown, and
our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide
by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very
reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get
heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it
seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such
rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or
add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of
flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is
equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is
that it avoids estimating any probabilities to be zero, even for events never observed in the data. Laplace
add-one smoothing now assigns too much probability to unseen words
NEW QUESTION 149
A newly joined team member John Smith in the Marketing team who currently does not have any access to the data requires read access to customers table, which of the following statements can be used to grant access.
- A. GRANT READ, USAGE TO [email protected] ON TABLE customers
- B. GRANT READ, USAGE ON TABLE customers TO [email protected]
- C. GRANT SELECT, USAGE ON TABLE customers TO [email protected]
- D. GRANT READ, USAGE ON customers TO [email protected]
- E. GRANT SELECT, USAGE TO [email protected] ON TABLE customers
Answer: C
Explanation:
Explanation
The answer is GRANT SELECT, USAGE ON TABLE customers TO [email protected] Data object privileges - Azure Databricks | Microsoft Docs
NEW QUESTION 150
......
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