[Nov 02, 2024] Get Free Updates Up to 365 days On Developing Databricks-Certified-Data-Engineer-Associate Braindumps [Q58-Q72]

November 2, 2024 admin 0

Categories : Databricks-Certified-Data-Engineer-Associate , Databricks

Rate this post

[Nov 02, 2024] Get Free Updates Up to 365 days On Developing Databricks-Certified-Data-Engineer-Associate Braindumps

Best Quality Databricks Databricks-Certified-Data-Engineer-Associate Exam Questions

Databricks Certified Data Engineer Associate Exam covers a wide range of topics, including data engineering fundamentals, data ingestion and processing, data warehousing and data lakes, data transformation and manipulation, and data quality and governance. Databricks-Certified-Data-Engineer-Associate exam is designed to provide a comprehensive understanding of Databricks and its features, and to ensure that candidates are equipped with the necessary skills to work with Databricks effectively.

Q58. Which of the following must be specified when creating a new Delta Live Tables pipeline?

A key-value pair configuration

The preferred DBU/hour cost

A path to cloud storage location for the written data

A location of a target database for the written data

At least one notebook library to be executed

Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode.
Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
References: Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

Q59. A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

Review the Permissions tab in the table’s page in Data Explorer

All of these options can be used to identify the owner of the table

Review the Owner field in the table’s page in Data Explorer

Review the Owner field in the table’s page in the cloud storage solution

There is no way to identify the owner of the table

Q60. A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

Q61. A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

GRANT VIEW ON CATALOG customers TO team;

GRANT CREATE ON DATABASE customers TO team;

GRANT USAGE ON CATALOG team TO customers;

GRANT CREATE ON DATABASE team TO customers;

GRANT USAGE ON DATABASE customers TO team;

The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals

Q62. A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = ‘FRANCE’;
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?

COMMENT “Contains PIT

511

“COMMENT PII”

TBLPROPERTIES PII

To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:
CREATE TABLE customersInFrance
USING DELTA
TBLPROPERTIES (‘PII’ = ‘true’)
AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = ‘FRANCE’;
The TBLPROPERTIES (‘PII’ = ‘true’) line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.
Reference:
Databricks documentation on Delta Lake: Delta Lake on Databricks

Q63. A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job’s current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.

They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.

They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.

There is no way to determine why a Job task is running slowly.

They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. Reference: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance

Q64. A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Databricks Repos automatically saves development progress

Databricks Repos provides the ability to comment on specific changes

Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Databricks Repos supports the use of multiple branches

Databricks Repos allows users to revert to previous versions of a notebook

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos – Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available – New ‘Files’ Feature in …, Databricks Repos – What it is and how we can use it | Adatis.

Q65. A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

if day_of_week = 1 and review_period:

if day_of_week = 1 and review_period = “True”:

if day_of_week == 1 and review_period == “True”:

if day_of_week == 1 and review_period:

if day_of_week = 1 & review_period: = “True”:

Q66. A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

Option A

Option B

Option C

Option D

Option E

Q67. In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

When the location of the data needs to be changed

When the target table is an external table

When the source table can be deleted

When the target table cannot contain duplicate records

When the source is not a Delta table

The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
References:
* 1: MERGE INTO | Databricks on AWS
* 2: [INSERT INTO | Databricks on AWS]
* 3: [External tables | Databricks on AWS]
* 4: [Temporary views | Databricks on AWS]
* 5: [Data sources | Databricks on AWS]

Q68. A data engineer runs a statement every day to copy the previous day’s sales into the table transactions. Each day’s sales are in their own file in the location “/transactions/raw”.
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

The names of the files to be copied were not included with the FILES keyword.

The previous day’s file has already been copied into the table.

The PARQUET file format does not support COPY INTO.

The COPY INTO statement requires the table to be refreshed to view the copied rows.

Q69. A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

Option A

Option B

Option C

Option D

Option E

Q70. A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

The table was managed

The table’s data was smaller than 10 GB

The table’s data was larger than 10 GB

The table was external

The table did not have a location

Explanation
managed tables files and metadata are managed by metastore and will be deleted when the table is dropped .
while external tables the metadata is stored in a external location.hence when a external table is dropped you clear off only the metadata and the files (data) remain.

Q71. Which of the following benefits is provided by the array functions from Spark SQL?

An ability to work with data in a variety of types at once

An ability to work with data within certain partitions and windows

An ability to work with time-related data in specified intervals

An ability to work with complex, nested data ingested from JSON files

An ability to work with an array of tables for procedural automation

The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. Reference: 1: Spark SQL, Built-in Functions – Apache Spark 2: Spark SQL Array Functions Complete List – Spark By Examples 3: Spark SQL Array Functions – Syntax and Examples – DWgeek.com 4: Spark SQL, Built-in Functions – Apache Spark : Spark SQL, Built-in Functions – Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks – The Databricks Blog]

Q72. A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id –
FROM STREAM(LIVE.customers)
WHERE loyalty_level = ‘high’;
Which of the following describes why the STREAM function is included in the query?

The STREAM function is not needed and will cause an error.

The table being created is a live table.

The customers table is a streaming live table.

The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.

The data in the customers table has been updated since its last run.

Explanation
https://docs.databricks.com/en/sql/load-data-streaming-table.html
Load data into a streaming table
To create a streaming table from data in cloud object storage, paste the following into the query editor, and then click Run:
SQL
Copy to clipboardCopy
/* Load data from a volume */
CREATE OR REFRESH STREAMING TABLE <table-name> AS
SELECT * FROM STREAM read_files(‘/Volumes/<catalog>/<schema>/<volume>/<path>/<folder>’)
/* Load data from an external location */
CREATE OR REFRESH STREAMING TABLE <table-name> AS
SELECT * FROM STREAM read_files(‘s3://<bucket>/<path>/<folder>’)

Loading …

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification for data professionals. Databricks Certified Data Engineer Associate Exam certification is designed to test the knowledge and skills of individuals who work with big data and data engineering. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics, including data modeling, ETL processes, data warehousing, and data analysis.

Databricks Exam Practice Test To Gain Brilliante Result: https://www.vceprep.com/Databricks-Certified-Data-Engineer-Associate-latest-vce-prep.html