初心者向けのDatabricks-Certified-Professional-Data-Engineer試験 [2023] 問題集でDatabricksのPDF問題 [Q11-Q31]

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初心者向けのDatabricks-Certified-Professional-Data-Engineer試験 [2023] 問題集でDatabricksのPDF問題

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新問題 11
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.

Create a new table with the required schema and new fields and use Delta Lake’s deep clone functionality to sync up changes committed to one table to the corresponding table.

Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.

Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

新問題 12
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

INSERT OVERWRITE replaces data by default, CREATE OR REPLACE replaces data and Schema by default

INSERT OVERWRITE replaces data and schema by default, CREATE OR REPLACEreplaces data by default

INSERT OVERWRITE maintains historical data versions by de-fault, CREATE OR REPLACEclears the historical data versions by default

INSERT OVERWRITE clears historical data versions by de-fault, CREATE OR REPLACE maintains the historical data versions by default

Both are same and results in identical outcomes

新問題 13
If you create a database sample_db with the statement CREATE DATABASE sample_db what will be the default location of the database in DBFS?

Default location, DBFS:/user/

Default location, /user/db/

Default Storage account

Statement fails “Unable to create database without location”

Default Location, dbfs:/user/hive/warehouse

新問題 14
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.

An external table will be created in the storage container mounted to /mnt/finance eda bucket.

A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

An managed table will be created in the storage container mounted to /mnt/finance eda bucket.

A managed table will be created in the DBFS root storage container.

新問題 15
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

The total average temperature across all sensors exceeded 120 on three consecutive executions of the query

Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query

The source query failed to update properly for three consecutive minutes and then restarted

The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query

The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

新問題 16
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?

preds.write.mode(“append”).saveAsTable(“churn_preds”)

preds.write.format(“delta”).save(“/preds/churn_preds”)

新問題 17
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

ALTER TABLE table_name OWNER to ‘group’

TRANSFER OWNER table_name to ‘group’

GRANT OWNER table_name to ‘group’*

ALTER OWNER ON table_name to ‘group’

GRANT OWNER On table_name to ‘group’

新問題 18
You are still noticing slowness in query after performing optimize which helped you to resolve the small files problem, the column(transactionId) you are using to filter the data has high cardinality and auto incrementing number. Which delta optimization can you enable to filter data effectively based on this column?

Create BLOOM FLTER index on the transactionId

Perform Optimize with Zorder on transactionId
(Correct)

transactionId has high cardinality, you cannot enable any optimization.

Increase the cluster size and enable delta optimization

Increase the driver size and enable delta optimization

新問題 19
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake’s time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Because Delta Lake’s delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.

Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.

新問題 20
A data engineer has created a Delta table as part of a data pipeline. Downstream data analysts now need
SELECT permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part of the Databricks Lakehouse Plat-form can
the data engineer use to grant the data analysts the appropriate access?

Jobs
B Dashboards

Data Explorer

Repos

Databricks Filesystem

新問題 21
Which of the following data workloads will utilize a Silver table as its source?

A job that aggregates cleaned data to create standard summary statistics

A job that queries aggregated data that already feeds into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A job that enriches data by parsing its timestamps into a human-readable format

A job that cleans data by removing malformatted records

新問題 22
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?

1. (spark.table(“sales”)
2. .groupBy(“store”)
3. .agg(sum(“sales”))
4. .writeStream
5. .option(“checkpointLocation”, checkpointPath)
6. .outputMode(“complete”)
7. .table(“aggregatedSales”)
8.)

1. (spark.table(“sales”)
2. .withColumn(“avgPrice”, col(“sales”) / col(“units”))
3. .writeStream
4. .option(“checkpointLocation”, checkpointPath)
5. .outputMode(“append”)
6. .table(“cleanedSales”)
7.)

1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option(“checkpointLocation”, checkpointPath)
4. .outputMode(“append”)
5. .table(“uncleanedSales”)
6. )

1. (spark.table(“sales”)
2. .agg(sum(“sales”),
3. sum(“units”))
4. .writeStream
5. .option(“checkpointLocation”, checkpointPath)
6. .outputMode(“complete”)
7. .table(“aggregatedSales”)
8. )

1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option(“checkpointLocation”, checkpointPath)
4. .outputMode(“append”)
5. .table(“uncleanedSales”)
6. )

新問題 23
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited

Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1

新問題 24
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?

Delete branch

Trigger Databricks CICD pipeline

Commit and push code

Create a pull request

Approve the pull request

新問題 25
Which of the following is correct for the global temporary view?

global temporary views cannot be accessed once the notebook is detached and attached

global temporary views can be accessed across many clusters

global temporary views can be still accessed even if the notebook is detached and at-tached

global temporary views can be still accessed even if the cluster is restarted

global temporary views are created in a database called temp database

新問題 26
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.

Naive Bayes

Laplace Smoothing

Logistic Regression

Linear Regression

新問題 27
How do you access or use tables in the unity catalog?

schema_name.table_name

schema_name.catalog_name.table_name

catalog_name.table_name

catalog_name.database_name.schema_name.table_name

catalog_name.schema_name.table_name

新問題 28
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Data plane

Control plane

Databricks Filesystem

Databricks web application

JDBC data source

新問題 29
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

Create jobs in different workspaces, so we can track the cost easily

Use Tags, during job creation so cost can be easily tracked

Use job logs to monitor and track the costs

Use workspace admin reporting

Use a single cluster for all the jobs, so cost can be easily tracked

新問題 30
What could be the expected output of query SELECT COUNT (DISTINCT *) FROM user on this table

2
(Correct)

NULL

新問題 31
Which of the following Structured Streaming queries is performing a hop from a bronze table to a Silver table?

1.(spark.table(“sales”).groupBy(“store”)
2..agg(sum(“sales”)).writeStream
3..option(“checkpointLocation”,checkpointPath)
4..outputMode(“complete”)
5..table(“aggregatedSales”))

1.(spark.table(“sales”).agg(sum(“sales”),sum(“units”))
2..writeStream
3..option(“checkpointLocation”,checkpointPath)
4..outputMode(“complete”)
5..table(“aggregatedSales”))

1.(spark.table(“sales”)
2..withColumn(“avgPrice”, col(“sales”) / col(“units”))
3..writeStream
4..option(“checkpointLocation”, checkpointPath)
5..outputMode(“append”)
6..table(“cleanedSales”))

1.(spark.readStream.load(rawSalesLocation)
2..writeStream
3..option(“checkpointLocation”, checkpointPath)
4..outputMode(“append”)
5..table(“uncleanedSales”) )

1.(spark.read.load(rawSalesLocation)
2..writeStream
3..option(“checkpointLocation”, checkpointPath)
4..outputMode(“append”)
5..table(“uncleanedSales”) )

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