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Databricks-Certified-Data-Engineer-Associate Latest Practice Questions, Accurate Databricks-Certified-Data-Engineer-Associate Answers
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The GAQM Databricks Certified Data Engineer Associate (Databricks-Certified-Data-Engineer-Associate) exam is a certification program designed for professionals who want to validate their expertise in data engineering with Databricks. Databricks is a cloud-based data processing and analytics platform that helps organizations to accelerate innovation and achieve better business outcomes. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the candidate's proficiency in performing various data engineering tasks using Databricks.
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Databricks-Certified-Data-Engineer-Associate exam is a comprehensive test of an individual's knowledge of Databricks and its related technologies, including Apache Spark, SQL, and Python. It evaluates the ability of candidates to design, build, and maintain data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data architects, and data scientists who want to showcase their skills and knowledge in working with Databricks.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q93-Q98):
NEW QUESTION # 93
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. O They can set up the dashboard's SQL endpoint to be serverless.
- B. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- C. O They can reduce the cluster size of the SQL endpoint.
- D. Q They can turn on the Auto Stop feature for the SQL endpoint.
Answer: D
Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
Reference:
Databricks documentation on SQL endpoints: SQL Endpoints in Databricks
NEW QUESTION # 94
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?
- A. Option B
- B. Option D
- C. Option A
- D. Option E
- E. Option C
Answer: D
NEW QUESTION # 95
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?
- A. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
- B. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
- C. They can schedule the query to run every 1 day from the Jobs UI.
- D. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
- E. They can schedule the query to run every 12 hours from the Jobs UI.
Answer: D
NEW QUESTION # 96
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?
- A. The COPY INTO statement requires the table to be refreshed to view the copied rows.
- B. The names of the files to be copied were not included with the FILES keyword.
- C. The previous day's file has already been copied into the table.
- D. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
- E. The PARQUET file format does not support COPY INTO.
Answer: C
Explanation:
Explanation
https://docs.databricks.com/en/ingestion/copy-into/index.html The COPY INTO SQL command lets you load data from a file location into a Delta table. This is a re-triable and idempotent operation; files in the source location that have already been loaded are skipped. if there are no new records, the only consistent choice is C no new files were loaded because already loaded files were skipped.
NEW QUESTION # 97
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
- A. Both teams would respond more quickly to ad-hoc requests
- B. Both teams would autoscale their work as data size evolves
- C. Both teams would be able to collaborate on projects in real-time
- D. Both teams would reorganize to report to the same department
- E. Both teams would use the same source of truth for their work
Answer: E
Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. Reference: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM
NEW QUESTION # 98
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