DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE PASS GUIDE, DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE VALID EXAM VCE FREE

Databricks-Certified-Data-Engineer-Associate Pass Guide, Databricks-Certified-Data-Engineer-Associate Valid Exam Vce Free

Databricks-Certified-Data-Engineer-Associate Pass Guide, Databricks-Certified-Data-Engineer-Associate Valid Exam Vce Free

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a comprehensive certification program designed to validate the skills and knowledge of data engineers in using Databricks to build and manage data pipelines, perform data analysis, and develop data-driven solutions. Databricks-Certified-Data-Engineer-Associate Exam is designed for professionals who work with big data and are responsible for designing, building, and maintaining data pipelines using Databricks.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
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 PARQUET file format does not support COPY INTO.
  • B. The names of the files to be copied were not included with the FILES keyword.
  • C. The COPY INTO statement requires the table to be refreshed to view the copied rows.
  • D. The previous day's file has already been copied into the table.
  • E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

Answer: D

Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS


NEW QUESTION # 40
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.
Which table can be created in this scenario?

  • A. A managed table where the catalog has managed location pointing to specific path in external location.
  • B. An external table where the location is pointing to specific path in external location.
  • C. An external table where the schema has managed location pointing to specific path in external location.
  • D. A managed table where the location is pointing to specific path in external location.

Answer: B


NEW QUESTION # 41
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

  • A. spark.table("sales")
  • B. spark.sql("sales")
  • C. spark.delta.table("sales")
  • D. SELECT * FROM sales
  • E. There is no way to share data between PySpark and SQL.

Answer: A

Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation


NEW QUESTION # 42
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

  • A. Data warehouse
  • B. Data lakehouse
  • C. None of these
  • D. Data lake
  • E. All of these

Answer: B

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics


NEW QUESTION # 43
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

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

Answer: E

Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.


NEW QUESTION # 44
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