Databricks Databricks-Certified-Data-Analyst-Associateリアル試験問題保証付き更新された問題集 [Q18-Q40]

Databricks Databricks-Certified-Data-Analyst-Associateリアル試験問題保証付き更新された問題集

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質問 # 18
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A. A refresh schedule with stakeholders included as subscribers
B. A refresh schedule with a Structured Streaming cluster
C. A refresh schedule with an interval of 10 minutes or less
D. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint

正解：C

解説：
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

質問 # 19
Which of the following approaches can be used to ingest data directly from cloud-based object storage?

A. It is not possible to directly ingest data from cloud-based object storage
B. Create an external table while specifying the object storage path to LOCATION
C. Create an external table while specifying the DBFS storage path to FROM
D. Create an external table while specifying the object storage path to FROM
E. Create an external table while specifying the DBFS storage path to PATH

正解：B

解説：
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.

質問 # 20
The stakeholders.customers table has 15 columns and 3,000 rows of dat
a. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view has been dropped.
B. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
C. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
D. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
E. The view has been converted into a table.

正解：C

解説：
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
Views do not store data themselves but reference data from underlying tables.
Logging out or being inactive does not delete or alter views.
Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn't changed).

質問 # 21
What is used as a compute resource for Databricks SQL?

A. Downstream BI tools integrated with Databricks SQL
B. Standard clusters
C. SQL warehouses
D. Single-node clusters

正解：C

質問 # 22
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

A. Histogram
B. IBar Chart
C. Line Chart
D. There is no default. The user must choose a visualization type.
E. Violin Chart

正解：B

解説：
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types

質問 # 23
Which statement about subqueries is correct?

A. Subqueries can retrieve data without requiring the creation of a table or view.
B. Subqueries can be used like other built-in functions to transform data into different data types.
C. Subqueries are not available in Databricks SQL
D. Subqueries can be used like other user-defined functions to transform data into different data types.

正解：A

解説：
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

質問 # 24
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

A. Histogram
B. IBar Chart
C. Line Chart
D. There is no default. The user must choose a visualization type.
E. Violin Chart

正解：B

質問 # 25
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

正解：A

質問 # 26
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age >= 75 AND country = 'canada';
Which of the following tables represents the output of the above command?

正解：E

解説：
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. Reference: The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

質問 # 27
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
B. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
C. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.
D. All data visualizations and counters must be created using Queries.

正解：D

解説：
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.

質問 # 28
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DELETE TABLE database_name.table_name;
B. DROP TABLE table_name FROM database_name;
C. DELETE TABLE table_name FROM database_name;
D. DROP DATABASE database_name;
E. DROP TABLE database_name.table_name;

正解：E

質問 # 29
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?

A. Visualization scale can be changed.
B. Borders can be added.
C. Colors can be changed.
D. Tooltips can be formatted.
E. Data Labels can be formatted.

正解：B

解説：
The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. Reference:
New chart visualizations in Databricks | Databricks on AWS

質問 # 30
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view has been dropped.
B. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
C. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
D. The view has been converted into a table.
E. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.

正解：A

解説：
The command you sent creates a TEMP VIEW, which is a type of view that is only visible and accessible to the session that created it. When the session ends or the user logs out, the TEMP VIEW is automatically dropped and cannot be queried anymore. Therefore, after logging back in two days later, the status of the stakeholders.eur_customers view is that it has been dropped and SELECT * FROM stakeholders.eur_customers will result in an error. The other options are not correct because:
A) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out.
C) The view is not available in the metastore, as it is a TEMP VIEW that is not registered in the metastore. The underlying data cannot be accessed with SELECT * FROM delta. stakeholders.eur_customers, as this is not a valid syntax for querying a Delta Lake table. The correct syntax would be SELECT * FROM delta.dbfs:/stakeholders/eur_customers, where the location path is enclosed in backticks. However, this would also result in an error, as the TEMP VIEW does not write any data to the file system and the location path does not exist.
D) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. Data in views are not automatically deleted after logging out, as views do not store any data. They are only logical representations of queries on base tables or other views.
E) The view has not been converted into a table, as there is no automatic conversion between views and tables in Databricks. To create a table from a view, you need to use a CREATE TABLE AS statement or a similar command. Reference: CREATE VIEW | Databricks on AWS, Solved: How do temp views actually work? - Databricks - 20136, temp tables in Databricks - Databricks - 44012, Temporary View in Databricks - BIG DATA PROGRAMMERS, Solved: What is the difference between a Temporary View an ...

質問 # 31
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Data engineer
B. SQL analyst
C. Data analyst
D. Business analyst
E. Business intelligence analyst

正解：A

解説：
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

質問 # 32
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable that never stops changing
B. A categorical variable in which the number of categories continues to increase over time
C. A quantitative variable that can take on an uncountable set of values
D. A quantitative variable Chat can take on a finite or countably infinite set of values

正解：C

解説：
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

質問 # 33
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable contains no outliers
B. When the variable is of the boolean type
C. When the variable contains a lot of extreme outliers
D. When the variable contains no missing values
E. When the variable is of the categorical type

正解：C

解説：
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. Reference: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

質問 # 34
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
B. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
C. Alerts don't work with queries that access tables.
D. Queries that return results based on dates cannot be used with Alerts.
E. Queries that use query parameters cannot be used with Alerts.

正解：E

解説：
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.

質問 # 35
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As an exact substitute with the same level of functionality
B. As a complementary tool for professional-grade presentations
C. As a complete replacement with additional functionality
D. As a substitute with less functionality
E. As a complementary tool for quick in-platform Bl work

正解：E

解説：
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

質問 # 36
How can a data analyst determine if query results were pulled from the cache?

A. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
B. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
C. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
D. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
E. Go to the Alerts tab and check the Cache Status alert.

正解：D

解説：
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

質問 # 37
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;

A. Option E
B. Option B
C. Option D
D. Option C
E. Option A

正解：E

解説：
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows

質問 # 38
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. Open-source formats
B. Scalable storage
C. Flexible schemas
D. ACID transactions
E. Data deletion

正解：D

解説：
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide

質問 # 39
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
B. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
C. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
D. Use Workflows to establish a cluster for Fivetran to interact with
E. Use Delta Live Tables to establish a cluster for Fivetran to interact with

正解：B

解説：
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

質問 # 40
......

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