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| Section | Objectives |
|---|
| Data Ingestion and ELT Development | - Handling structured and semi-structured data - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark
|
| Productionizing Data Pipelines | - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs
|
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
|
| Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering)
|
| Data Governance and Quality | - Unity Catalog basics - Data access control and governance - Data quality concepts and management
|
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q56-Q61):
NEW QUESTION # 56
Which of the following commands will return the number of null values in the member_id column?
- A. SELECT count(member_id) - count_null(member_id) FROM my_table;
- B. SELECT null(member_id) FROM my_table;
- C. SELECT count(member_id) FROM my_table;
- D. SELECT count_if(member_id IS NULL) FROM my_table;
- E. SELECT count_null(member_id) FROM my_table;
Answer: D
Explanation:
Explanation
https://docs.databricks.com/en/sql/language-manual/functions/count.html Returns A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL.
If DISTINCT duplicate rows are not counted.
NEW QUESTION # 57
An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing. Which strategy will reduce data egress costs associated with cross-cloud data sharing?
- A. Configure VPN connection between AWS and Azure for faster data sharing
- B. Using Delta Sharing without any additional configurations
- C. Sharing data via pre-signed URLs without monitoring egress costs
- D. Migrating the dataset to Cloudflare R2 object storage before sharing
Answer: B
Explanation:
Delta Sharing natively supports cross-cloud data access without requiring data duplication or special networking setups. It allows secure sharing of datasets across different cloud providers while minimizing egress costs, since recipients query the data directly from its source location rather than transferring large volumes unnecessarily.
NEW QUESTION # 58
A data engineer wants to reduce costs and optimize cloud spending. The data engineer has decided to use Databricks Serverless for lowering cloud costs while maintaining existing SLAs.
What is the first step in migrating to Databricks Serverless?
- A. A frequently running and efficient Scala-based data transformation pipeline compatible with the latest Databricks runtime and Unity Catalog
- B. Low frequency BI Dashboarding and Adhoc SQL Analytics
- C. Legacy Ingestion pipelines that include ingestion from sources API's, files, JDBC/ODBC connections
- D. A frequently running and efficient Python-based data transformation pipeline compatible with the latest Databricks runtime and Unity Catalog
Answer: D
Explanation:
The first workloads to migrate to Databricks Serverless should be frequently running, efficient pipelines that are already compatible with the latest Databricks runtime and Unity Catalog. This ensures smooth migration while maintaining SLAs, and Python-based pipelines are the most common and fully supported starting point.
NEW QUESTION # 59
Which of the following commands will return the number of null values in the member_id column?
- A. SELECT count(member_id) - count_null(member_id) FROM my_table;
- B. SELECT null(member_id) FROM my_table;
- C. SELECT count(member_id) FROM my_table;
- D. SELECT count_if(member_id IS NULL) FROM my_table;
- E. SELECT count_null(member_id) FROM my_table;
Answer: D
Explanation:
To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL.
Option E will not work because there is no count_null function in Spark SQL. References:
* Built-in Functions - Spark SQL, Built-in Functions
* count_if - Spark SQL, Built-in Functions
NEW QUESTION # 60
Which of the following describes the type of workloads that are always compatible with Auto Loader?
- A. Dashboard workloads
- B. Batch workloads
- C. Machine learning workloads
- D. Streaming workloads
- E. Serverless workloads
Answer: D
Explanation:
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables
NEW QUESTION # 61
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