QUESTION: 5

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?

Business analyst
SQL analyst
Data engineer
Business intelligence analyst
Data analyst

Answer(s): C

Explanation:

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

Reveal Solution Next Question

QUESTION: 6

A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.

Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

Reduce the SQL endpoint cluster size
Increase the SQL endpoint cluster size
Turn off the Auto stop feature
Increase the minimum scaling value
Use a Serverless SQL endpoint

Answer(s): E

Explanation:

A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster.

Reference:

Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters

Reveal Solution Next Question

QUESTION: 7

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 minute.

A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.

Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

The required compute resources could be costly
The gold-level tables are not appropriately clean for business reporting
The streaming data is not an appropriate data source for a dashboard
The streaming cluster is not fault tolerant
The dashboard cannot be refreshed that quickly

Answer(s): A

Explanation:

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:

B) The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.

C) The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.

D) The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.

E) The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources.

Reference:

Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

Reveal Solution Next Question

QUESTION: 8

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

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

Answer(s): E

Explanation:

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.

Reference:

External tables

Reveal Solution Next Question

Free Databricks-Certified-Data-Analyst-Associate Exam Braindumps (page: 2)

QUESTION: 5

Explanation:

Reference:

QUESTION: 6

Explanation:

Reference:

QUESTION: 7

Explanation:

Reference:

QUESTION: 8

Explanation:

Reference: