Databricks Certified Data Analyst Associate 시험 - Databricks실제시험문제와 답 - 45문항

Question No : 1

Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A.Cloud-specific integrations
B.Simplified governance
C.Ability to scale storage
D.Ability to scale workloads
E.Avoiding vendor lock-in

정답:
Explanation:
Option E is correct. A major benefit of open source technologies and open data formats is avoiding vendor lock-in. Databricks supports open formats and interfaces so data can be used across tools and systems rather than being locked into a proprietary platform. Cloud integrations, governance, and workload scalability are Databricks benefits, but the specific benefit of embracing open source is avoiding vendor lock-in. Official Databricks extract: “Using open data formats and interfaces helps to avoid” vendor lock-in, and Databricks states that no proprietary data formats are used because Delta Lake and Iceberg are open source.

Question No : 2

Which of the following describes the relationship between Gold tables and Silver tables?

A.Gold tables are more likely to contain aggregations than Silver tables.
B.Gold tables are more likely to contain valuable data than Silver tables.
C.Gold tables are more likely to contain a less refined view of data than Silver tables.
D.Gold tables are more likely to contain more data than Silver tables.
E.Gold tables are more likely to contain truthful data than Silver tables.

정답:
Explanation:
Option A is correct. Silver tables are cleaned, validated, and enriched versions of data, often still retaining detailed records. Gold tables are typically business-ready, analytics-focused, and more likely to contain aggregations, dimensional models, and reporting-ready metrics. Official Databricks extract: Silver is associated with “Data cleaning and validation,” while Gold is associated with “Dimensional modeling and aggregation.” Databricks also states that Gold data is often highly aggregated and tailored for analytics and reporting.

Question No : 3

Which data lakehouse feature results in improved data quality over a traditional data lake?

A.A data lakehouse stores data in open formats.
B.A data lakehouse allows the use of SQL queries to examine data.
C.A data lakehouse provides storage solutions for structured and unstructured data.
D.A data lakehouse supports ACID-compliant transactions.

정답:
Explanation:
Option D is correct. ACID-compliant transactions improve reliability and data quality because writes are consistent and atomic rather than leaving partially written or corrupted states. Open formats and SQL access are important lakehouse capabilities, but the feature most directly tied to improved data quality over a traditional data lake is ACID transactions. Official Databricks extract: Delta Lake extends Parquet with a transaction log for “ACID transactions,” and Databricks states that Delta Lake adds ACID transactions and schema evolution for reliable, high-quality data.

Question No : 4

A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A.Workspace audit logs
B.Driver’s log file
C.Ganglia
D.Cluster Event Log
E.Executor’s log file

정답:
Explanation:
Option D is correct. Cluster resizing caused by autoscaling is recorded as a cluster activity event, so the administrator should review the Cluster Event Log. Driver and executor logs are useful for application/runtime troubleshooting, not for the cluster resizing timeline. Ganglia shows metrics but is not the event timeline. Official Databricks documentation identifies cluster activity events as the place to retrieve cluster activity, and Databricks autoscaling documentation describes resize events such as “Cluster resize request started.”

Question No : 5

Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

A.TRANSFORM
B.PIVOT
C.SUM
D.CONVERT
E.WHERE

정답:
Explanation:
Option B is correct. PIVOT rotates unique values from rows into separate columns, which is exactly the process of converting long-format data into wide-format data. UNPIVOT does the reverse. SUM is an aggregate function, WHERE filters rows, and TRANSFORM is not the SQL keyword used for this reshaping task. Official Databricks extract: the PIVOT clause “rotat[es] unique values from a column into separate columns.”

Question No : 6

Which of the following commands will return the location of database customer360?

A.DESCRIBE LOCATION customer360;
B.DROP DATABASE customer360;
C.DESCRIBE DATABASE customer360;
D.ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user');
E.USE DATABASE customer360;

정답:
Explanation:
Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema’s “location on the filesystem.”

Question No : 7

Which of the following is stored in the Databricks customer’s cloud account?

A.Databricks web application
B.Cluster management metadata
C.Repos
D.Data
E.Notebooks

정답:
Explanation:
Option D is correct. Customer data is stored in the customer’s cloud account, such as object storage controlled by the customer. Databricks control-plane services manage platform components, while the customer’s data resides in the customer-controlled storage layer .
Official Databricks extract:
“Databricks doesn’t store your primary customer data. That lives in Amazon S3 or other systems you control.”

Question No : 8

A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

A.Merge
B.Push
C.Pull
D.Commit
E.Clone

정답:
Explanation:
Option C is correct. To retrieve changes from a remote central Git repository into a local Databricks Git folder/Repo, the correct Git operation is Pull. Push sends local commits to the remote repository. Commit records local changes. Clone creates a new local copy. Merge combines branches but is not the operation described here. Official Databricks extract: “To pull changes from the remote Git repository, click Pull.”

Question No : 9

A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A.Spark SQL Table
B.View
C.Database
D.Temporary view
E.Delta Table

정답:
Explanation:
Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a “virtual table that has no physical data,” and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

Question No : 10

A data analyst has been asked to use the below table sales_table to rank products within region by the sales.
Input table:
region product sales
WEST A 1880.59
EAST A 2045.99
EAST B 4583.23
WEST B 3391.19
The result of the query should look like this:
region product rank
EAST B 1
EAST A 2
WEST B 1
WEST A 2
Which query will accomplish this task?
A . SELECT
region , product , RANK() OVER (
ORDER BY sales DESC
) AS rank
FROM sales_table;
B . SELECT
region , product , RANK() OVER (
PARTITION BY product
ORDER BY sales DESC
) AS rank
FROM sales_table;
C . SELECT
region , product , RANK() OVER (
PARTITION BY region
) AS rank
FROM sales_table;
D . SELECT
region , product , RANK() OVER (
PARTITION BY region
ORDER BY sales DESC
) AS rank
FROM sales_table;
A. Option A
B. Option B
C. Option C
D. Option D

정답: D
Explanation:
Option D is correct. The phrase “rank products within region by the sales” means each region must be ranked separately, so the window must use PARTITION BY region. The ranking must be based on highest sales first, so the window must use ORDER BY sales DESC.
Option A ranks across all regions together.
Option B ranks within product, not within region.
Option C does not specify the sales ordering. Official Databricks extract: rank() “returns the rank of a value compared to all values in the partition,” and its OVER clause must include ORDER BY.

Question No : 11

A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A.Databricks Filesystem
B.Jobs
C.Dashboards
D.Repos
E.Data Explorer

정답:
Explanation:
Option E is correct. In the current Databricks UI, Data Explorer is known as Catalog Explorer. It is the location used to browse catalogs, schemas, tables, and views, review object metadata, and request or inspect access-related information. DBFS, Jobs, Dashboards, and Repos are not the table-permission review location. Official Databricks extract: users can navigate the catalog tree, open an object’s page, and request additional privileges from Catalog Explorer.

Question No : 12

Which of the following data workloads will utilize a Gold table as its source?

A.A job that enriches data by parsing its timestamps into a human-readable format.
B.A job that aggregates uncleaned data to create standard summary statistics.
C.A job that cleans data by removing malformatted records.
D.A job that queries aggregated data designed to feed into a dashboard.
E.A job that ingests raw data from a streaming source into the Lakehouse.

정답:
Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer “consists of aggregated data tailored for analytics and reporting” and is optimized for “queries and dashboards.”

Question No : 13

A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
CREATE VIEW user_ltv_no_minors AS
SELECT email, age, ltv
FROM user_ltv
WHERE
CASE
WHEN is_member("auditing") THEN TRUE
ELSE age >= 18
END;
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors;
Which statement describes the results returned by this query?

A.All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
B.All age values less than 18 will be returned as null values, all other columns will be returned with the values in user_ltv.
C.All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
D.All records from all columns will be displayed with the values in user_ltv.
E.All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

정답:
Explanation:
Option A is correct. The user is not a member of the auditing group, so is_member("auditing") evaluates to false and the ELSE age >= 18 branch controls the filter. Rows with age >= 18 are returned; rows under 18 are omitted. “Age greater than 17” is equivalent to age >= 18 for integer ages. Official Databricks extract: is_member() “returns TRUE if the current user is a member” of the specified group, and Databricks describes dynamic views as views that can filter rows based on group membership.

Question No : 14

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?

A.They can increase the cluster size of the SQL endpoint.
B.They can increase the maximum bound of the SQL endpoint’s scaling range.
C.They can turn on the Auto Stop feature for the SQL endpoint.
D.They can turn on the Serverless feature for the SQL endpoint.
E.They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”

정답:
Explanation:
Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: “You can increase the maximum clusters if you want to handle more concurrent users,” and Databricks recommends monitoring queued queries and adjusting maximum clusters.

Question No : 15

A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

A.They can set up an Alert with a custom template.
B.They can set up an Alert with a new email alert destination.
C.They can set up an Alert with one-time notifications.
D.They can set up an Alert with a new webhook alert destination.
E.They can set up an Alert without notifications.

정답:
Explanation:
Option D is correct. Databricks SQL alerts run a query on a schedule, evaluate a condition, and notify destinations when the condition is met. Since the requirement is notification through a messaging webhook, the alert should use a webhook alert destination. Official Databricks extract: alerts “run queries on a schedule and notify you when a condition that you define is met.” Databricks notification documentation also states that admins can configure alternate notification destinations using webhooks.

Databricks Databricks Certified Data Analyst Associate 시험