Databricks Databricks Certified Data Engineer Professional Dumps - Shortcut Key To Success

Databricks Databricks Certified Data Engineer Professional Dumps – Shortcut Key To Success

Jonnie Karnath2023-04-21T05:02:59+00:00

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1. )

If the data engineer only wants the query to execute a single micro-batch to process all of the available data, which of the following lines of code should the data engineer use to fill in the blank?

.processingTime(1)

.processingTime("once")

.trigger(processingTime="once")

.trigger(once=True)

.trigger(continuous="once")

2. FROM raw_table;

3. 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?

Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them

Enable back_fill = TRUE to reprocess the data

Delete the checkpoint folder and run the autoloader again

Autoloader automatically re-processes data that was not loaded

Manually re-load the data

4. table("target_table")

checkpointlocation, complete, True

targetlocation, overwrite, True

checkpointlocation, True, overwrite

checkpointlocation, True, complete

checkpointlocation, overwrite, True

5. You are asked to write a python function that can read data from a delta table and return the Data-Frame, which of the following is correct?

Python function cannot return a DataFrame

Write SQL UDF to return a DataFrame

Write SQL UDF that can return tabular data

Python function will result in out of memory error due to data volume

Python function can return a DataFrame

6. A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this process.

Which of the following steps must the data engineer take as part of implementing this pipeline using Delta Live Tables?

They need to create a Delta Live Tables pipeline from the Jobs page

They need to refactor their notebook to use Python and the dlt library

They need to create a Delta Live tables pipeline from the Compute page

They need to create a Delta Live Tables pipeline from the Data page

They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword

7. A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes, the second Job fails when the first Job does not complete by 12:30 AM.

Which of the following approaches can the data engineer use to avoid this problem?

They can set up a retry policy on the first Job to help it run more quickly

They can use cluster pools to help the Jobs run more efficiently

They can limit the size of the output in the second Job so that it will not fail as easily

They can set up the data to stream from the first Job to the second Job

They can utilize multiple tasks in a single job with a linear dependency

8. What is the type of table created when you issue SQL DDL command CREATE TABLE sales (id int, units int)

Query fails due to missing location

Query fails due to missing format

Managed Delta table

External Table

Managed Parquet table

9. Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?

MERGE INTO table_name

COPY INTO table_name

UPDATE table_name

INSERT INTO OVERWRITE table_name

INSERT IF EXISTS table_name

10. Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?

row_count = spark.sql("select count(*) from table").collect()[0][0]

assert (row_count = 10, "Row count did not match")

assert if (row_count = 10, "Row count did not match")

assert row_count == 10, "Row count did not match"

assert if row_count == 10, "Row count did not match"

assert row_count = 10, "Row count did not match"

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11. In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

Enable unity catalog feature in workspace settings

Migrate/upgrade objects in workspace managed/external tables/view to unity catalog

Upgrade to DBR version 15.0

Copy data from workspace to unity catalog

Upgrade workspace to Unity catalog

12. Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard

The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 A

Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.

A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.

Use Incremental refresh to run at 8 AM every day.

13. delta.sql("select * from table")

14. Identify one of the below statements that can query a delta table in PySpark Dataframe API

Spark.read.mode("delta").table("table_name")

Spark.read.table.delta("table_name")

Spark.read.table("table_name")

Spark.read.format("delta").LoadTableAs("table_name")

Spark.read.format("delta").TableAs("table_name")

15. 1.You were asked to setup a new all-purpose cluster, but the cluster is unable to start which of the following steps do you need to take to identify the root cause of the issue and the reason why the cluster was unable to start?

Check the cluster driver logs

Check the cluster event logs

Workspace logs

Storage account

Data plane

16. What is the purpose of a gold layer in Multi-hop architecture?

Optimizes ETL throughput and analytic query performance

Eliminate duplicate records

Preserves grain of original data, without any aggregations

Data quality checks and schema enforcement

Powers ML applications, reporting, dashboards and adhoc reports.

17. Unity catalog simplifies managing multiple workspaces, by storing and managing permissions and ACL at _______ level

Workspace

Account (Correct)

Storage

Data pane

Control pane

18. Your colleague was walking you through how a job was setup, but you noticed a warning message that said, “Jobs running on all-purpose cluster are considered all purpose compute", the colleague was not sure why he was getting the warning message, how do you best explain this warning mes-sage?

All-purpose clusters cannot be used for Job clusters, due to performance issues.

All-purpose clusters take longer to start the cluster vs a job cluster

All-purpose clusters are less expensive than the job clusters

All-purpose clusters are more expensive than the job clusters

All-purpose cluster provide interactive messages that can not be viewed in a job

19. You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

Setup an additional job to run ahead of the actual job so the cluster is running second job starts

Use the Databricks cluster pools feature to reduce the startup time

Use Databricks Premium edition instead of Databricks standard edition

Pin the cluster in the cluster UI page so it is always available to the jobs

Disable auto termination so the cluster is always running

20. What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

DELTA LIVE TABLES do not support job cluster

Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook

Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file

Use Pipeline creation UI, select a new pipeline and job cluster

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21. A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.

The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL

query returns the number of minutes since the job's most recent runtime.

Which of the following approaches can enable the data engineering team to be notified if the ELT job has not been run in an hour?

They can set up an Alert for the query to notify when the ELT job fails

They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater than 60

They can set up an Alert for the accompanying dashboard to notify when it has not refreshed in 60 minutes

They can set up an Alert for the query to notify them: if the returned value is greater than 60

This type of alerting is not possible in Databricks

22. Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file. (Correct)

All of the data and log are stored in a single parquet file

All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.

All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.

All of the data is stored into one parquet file, log files are broken down into one or many json files.

23. Which of the following data workloads will utilize a Bronze table as its destination?

A job that aggregates cleaned data to create standard summary statistics

A job that queries aggregated data to publish key insights into a dashboard

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

A job that develops a feature set for a machine learning application

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

24. Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data

warehouse?

A data lakehouse couples storage and compute for complete control

A data lakehouse provides a relational system of data management

A data lakehouse utilizes proprietary storage formats for data

A data lakehouse enables both batch and streaming analytics

A data lakehouse captures snapshots of data for version control purposes

25. table(table_name))

checkpointlocation, schemalocation

checkpointlocation, cloudfiles.schemalocation

schemalocation, checkpointlocation

cloudfiles.schemalocation, checkpointlocation

cloudfiles.schemalocation, cloudfiles.checkpointlocation

26. FILEFORMAT = CSV

COPY INTO only works one time data load

Run REFRESH TABLE sales before running COPY INTO

COPY INTO did not detect new files after the last load

Use incremental = TRUE option to load new files

COPY INTO does not support incremental load, use AUTO LOADER

27. Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement

CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)

________________

LOCATION “dbfs:/mnt/sales/*.csv”

FORMAT CSV OPTIONS (“true”,”|”)

USING CSV TYPE (“true”,”|”)

USING CSV OPTIONS (header =“true”, delimiter = ”|”) (Correct)

FORMAT CSV FORMAT TYPE (header =“true”, delimiter = ”|”)

FORMAT CSV TYPE (header =“true”, delimiter = ”|”)

28. 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?

AUTO LOADER

JOBS and TASKS

SQL Endpoints

DELTA LIVE TABLES

STRUCTURED STREAMING with MULTI HOP

29. You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

Databricks Repos allows multiple users to make changes

Databricks Repos allows merge and conflict resolution

Databricks Repos has a built-in version control system

Databricks Repos automatically saves changes

Databricks Repos allow you to add comments and select the changes you want to commit.

30. You would like to build a spark streaming process to read from a Kafka queue and write to a Delta table every 15 minutes, what is the correct trigger option

trigger("15 minutes")

trigger(process "15 minutes")

trigger(processingTime = 15)

trigger(processingTime = "15 Minutes")

trigger(15)

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31. Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer experienced in Databricks suggests there is a better alternative for this type of collaboration .

Which of the following supports the senior data engineer's claim?

Databricks Notebooks support commenting and notification comments

Databricks Notebooks support the creation of interactive data visualizations

Databricks Notebooks support real-time co-authoring on a single notebook

Databricks Notebooks support the use of multiple languages in the same notebook

Databricks Notebooks support automatic change-tracking and versioning

32. Which of the following statements describes Delta Lake?

Delta Lake is an open source platform to help manage the complete machine learning lifecycle

Delta Lake is an open format storage layer that delivers reliability, security, and per-formance

Delta Lake is an open source data storage format for distributed data

Delta Lake is an open source analytics engine used for big data workloads

Delta Lake is an open format storage layer that processes data

33. Which of the following statements are correct on how Delta Lake implements a lake house?

Delta lake uses a proprietary format to write data, optimized for cloud storage

Using Apache Hadoop on cloud object storage

Delta lake always stores meta data in memory vs storage

Delta lake uses open source, open format, optimized cloud storage and scalable meta data

Delta lake stores data and meta data in computes memory

34. A data engineering team needs to query a Delta table to extract rows that all meet the same condition.

However, the team has noticed that the query is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located throughout each of the data files.

Based on the scenario, which of the following optimization techniques could speed up the query?

Tuning the file size

Bin-packing

Data skipping

Write as a Parquet file

Z-Ordering

35. What is the main difference between the bronze layer and silver layer in a medallion architecture?

Duplicates are removed in bronze, schema is applied in silver

Silver may contain aggregated data

Bronze is raw copy of ingested data, silver contains data with production schema and optimized for ELT/ETL throughput

Bad data is filtered in Bronze, silver is a copy of bronze data

36. You are designing an analytical to store structured data from your e-commerce platform and un-structured data from website traffic and app store, how would you approach where you store this data?

Use traditional data warehouse for structured data and use data lakehouse for un-structured data.

Data lakehouse can only store unstructured data but cannot enforce a schema

Data lakehouse can store structured and unstructured data and can enforce schema

Traditional data warehouses are good for storing structured data and enforcing schema

37. Which of the following data workloads will utilize a gold table as its source?

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

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 aggregates cleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

38. A particular job seems to be performing slower and slower over time, the team thinks this started to happen when a recent production change was implemented, you were asked to take look at the job history and see if we can identify trends and root cause, where in the workspace UI can you perform this analysis?

Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run

Under jobs UI select the job cluster, under spark UI select the application job logs, then you can access last 60 day historical runs

Under Workspace logs, select job logs and select the job you want to monitor to view the last 60 day historical runs

Under Compute UI, select Job cluster and select the job cluster to see last 60 day his-torical runs

Historical job runs can only be accessed by REST API

39. Which of the following data workloads will utilize a silver table as its source?

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

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 aggregates cleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

40. What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

1/3

2/3

1/6

2/6

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41. Which of the following commands results in the successful creation of a view on top of the delta stream (stream on delta table)?

Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")

Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw ")

Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("strea ming_vw")

Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("stre aming_vw")

Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")

You can not create a view on streaming data source.

42. A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue is affecting all of their sequentially run queries. They ask the data engineering team for help. The data engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not used by any other user.

Which of the following approaches can the data engineering team use to improve the latency of the data analyst's queries?

They can increase the maximum bound of the SQL endpoint's scaling range

They can increase the cluster size of the SQL endpoint

They can turn on the Auto Stop feature for the SQL endpoint

They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to "Reliability Optimized"

They can turn on the Serverless feature for the SQL endpoint

43. SELECT * FROM transactions

D. COPY OVERWRITE transactions from VERSION as of 3

44. What is the main difference between AUTO LOADER and COPY INTO?

COPY INTO supports schema evolution.

AUTO LOADER supports schema evolution.

COPY INTO supports file notification when performing incremental loads.

AUTO LOADER supports reading data from Apache Kafka

AUTO LOADER Supports file notification when performing incremental loads.

45. The data engineering team is using a bunch of SQL queries to review data quality and monitor the ETL job every day, which of the following approaches can be used to set up a schedule and auto-mate this process?

They can schedule the query to run every 1 day from the Jobs UI

They can schedule the query to refresh every 1 day from the query’s page in Databricks SQ

They can schedule the query to run every 12 hours from the Jobs U

They can schedule the query to refresh every 1 day from the SQL endpoint’s page in Databricks SQ

They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL

46. Which of the following results in the creation of an external table?

CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL

CREATE TABLE transactions (id int, desc string)

CREATE EXTERNAL TABLE transactions (id int, desc string)

CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL

CREATE TABLE transactions (id int, desc string) LOCATION '/mnt/delta/transactions'

47. table("uncleanedSales")

48. Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines that utilize Spark and Delta Lake on Databricks?

The ability to write pipelines in Python and/or SQL

The ability to declare and maintain data table dependencies

The ability to automatically scale compute resources

The ability to access previous versions of data tables

The ability to perform batch and streaming queries

49. You are asked to setup an AUTO LOADER to process the incoming data, this data arrives in JSON format and get dropped into cloud object storage and you are required to process the data as soon as it arrives in cloud storage, which of the following statements is correct

AUTO LOADER is native to DELTA lake it cannot support external cloud object storage

AUTO LOADER has to be triggered from an external process when the file arrives in the cloud storage

AUTO LOADER needs to be converted to a Structured stream process

AUTO LOADER can only process continuous data when stored in DELTA lake

AUTO LOADER can support file notification method so it can process data as it arrives

50. What is the best way to describe a data lakehouse compared to a data warehouse?

A data lakehouse provides a relational system of data management

A data lakehouse captures snapshots of data for version control purposes.

A data lakehouse couples storage and compute for complete control.

A data lakehouse utilizes proprietary storage formats for data.

A data lakehouse enables both batch and streaming analytics.

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51. A data engineer has set up a notebook to automatically process using a Job. The data engineer's manager wants to version control the schedule due to its complexity.

Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule?

They can download the JSON description of the Job from the Job's page

They can submit the Job once on an all-purpose cluster

They can link the Job to notebooks that are a part of a Databricks Repo

They can submit the Job once on a Job cluster

They can download the XML description of the Job from the Job's page

52. 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

53. A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL

What is the expected behavior when a batch of data containing data that violates these constraints is processed?

Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Records that violate the expectation cause the job to fail

Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.

Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

54. You are currently looking at a table that contains data from an e-commerce platform, each row contains a list of items(Item number) that were present in the cart, when the customer makes a change to the cart the entire information is saved as a separate list and appended to an existing list for the duration of the customer session, to identify all the items customer bought you have to make a unique list of items, you were asked to create a unique item’s list that was added to the cart by the user, fill in the blanks of below query by choosing the appropriate higher-order function?

Note: See below sample data and expected output.

Schema: cartId INT, items Array<INT>

Fill in the blanks:

Fill in the blanks:

SELECT cartId, _(_(items)) FROM carts

ARRAY_UNION, ARRAY_DISCINT

ARRAY_DISTINCT, ARRAY_UNION

ARRAY_DISTINCT, FLATTEN

FLATTEN, ARRAY_DISTINCT

ARRAY_DISTINCT, ARRAY_FLATTEN

55. AS SELECT * FROM table_name

56. Which of the following scenarios is the best fit for the AUTO LOADER solution?

Efficiently process new data incrementally from cloud object storage

Incrementally process new streaming data from Apache Kafa into delta lake

Incrementally process new data from relational databases like MySQL

Efficiently copy data from data lake location to another data lake location

Efficiently move data incrementally from one delta table to another delta table

57. What is the purpose of a silver layer in Multi hop architecture?

Replaces a traditional data lake

Efficient storage and querying of full and unprocessed history of data

A schema is enforced, with data quality checks.

Refined views with aggregated data

Optimized query performance for business-critical data

58. You are currently working with the application team to setup a SQL Endpoint point, once the team started consuming the SQL Endpoint you noticed that during peak hours as the number of concur-rent users increases you are seeing degradation in the query performance and the same queries are taking longer to run, which of the following steps can be taken to resolve the issue?

They can turn on the Serverless feature for the SQL endpoint.

They can increase the maximum bound of the SQL endpoint’s scaling range.

They can increase the cluster size(2X-Small to 4X-Large) of the SQL endpoint.

They can turn on the Auto Stop feature for the SQL endpoint.

They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy from “Cost optimized” to “Reliability Optimized.”

59. Which of the following tool provides Data Access control, Access Audit, Data Lineage, and Data discovery?

DELTA LIVE Pipelines

Unity Catalog

Data Governance

DELTA lake

Lakehouse

60. Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos ？

Trigger Databricks Repos pull API to update the latest version

Commit and push code

Create and edit code

Create a new branch

Pull request and review process

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61. While investigating a performance issue, you realized that you have too many small files for a given table, which command are you going to run to fix this issue

COMPACT table_name

VACUUM table_name

MERGE table_name

SHRINK table_name

OPTIMIZE table_name

62. Once a cluster is deleted, below additional actions need to performed by the administrator

Remove virtual machines but storage and networking are automatically dropped

Drop storage disks but Virtual machines and networking are automatically dropped

Remove networking but Virtual machines and storage disks are automatically dropped

Remove logs

No action needs to be performed. All resources are automatically removed.

63. return x

64. 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

65. When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query.

How can we address this limitation?

UNCACHE TABLE table_name

CACHE TABLE table_name

REFRESH TABLE table_name

BROADCAST TABLE table_name

CLEAR CACH table_name

66. Create a schema called bronze using location ‘/mnt/delta/bronze’, and check if the schema exists before creating.

CREATE SCHEMA IF NOT EXISTS bronze LOCATION '/mnt/delta/bronze'

CREATE SCHEMA bronze IF NOT EXISTS LOCATION '/mnt/delta/bronze'

if IS_SCHEMA('bronze'): CREATE SCHEMA bronze LOCATION '/mnt/delta/bronze'

Schema creation is not available in metastore, it can only be done in Unity catalog UI

Cannot create schema without a database

67. The data engineering team is using a SQL query to review data completeness every day to monitor the ETL job, and query output is being used in multiple dashboards which of the following approaches can be used to set up a schedule and automate this process?

They can schedule the query to run every day from the Jobs U

They can schedule the query to refresh every day from the query’s page in Databricks SQL

They can schedule the query to run every 12 hours from the Jobs U

They can schedule the query to refresh every day from the SQL endpoint’s page in Databricks SQ

They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL

68. 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?

Kevin can not grant access to Steven since he does not have security admin privilege

Kevin although is the owner but does not have ALL PRIVILEGES permission

Kevin can grant access to the view, because he is the owner of the view and the under-lying table

Kevin can not grant access to Steven since he does have workspace admin privilege

Steve will also require SELECT access on the underlying table

69. 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?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

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

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

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)

70. Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array.

So what is the primary reason of the hashing trick for building classifiers?

It creates the smaller models

It requires the lesser memory to store the coefficients for the model

It reduces the non-significant features e.g. punctuations

Noisy features are removed

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71. The data engineering team noticed that one of the job normally finishes in 15 mins but gets stuck randomly when reading remote databases due to a network packet drop, which of the following steps can be used to improve the stability of the job?

Use Databrick REST API to monitor long running jobs and issue a kill command

Use Jobs runs, active runs UI section to monitor and kill long running job

Modify the task, to include a timeout to kill the job if it runs more than 15 mins.

Use Spark job time out setting in the Spark UI

Use Cluster timeout setting in the Job cluster UI

72. )

The code block is returning an error.

Which of the following changes should be made to the code block to configure the block to successfully perform a streaming read?

The .read line should be replaced with .readStream

The .format(' cloudFiles") line should be replaced with .format("stream")

A new .stream line should be added after the spark line

A new .stream line should be added after the .read line

A new .stream line should be added after the .load(dataSource) line

73. SELECT * FROM ____________________

SELECT * FROM f{schema_name.table_name}

SELECT * FROM {schem_name.table_name}

SELECT * FROM ${schema_name}.${table_name}

SELECT * FROM schema_name.table_name

74. table("uncleanedSales") )

75. What are the different ways you can schedule a job in Databricks workspace?

Continuous, Incremental

On-Demand runs, File notification from Cloud object storage

Cron, On Demand runs

Cron, File notification from Cloud object storage

Once, Continuous

76. 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

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