VALID BRAINDUMPS DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE PPT | DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE EXAM PDF

Valid Braindumps Databricks-Certified-Data-Engineer-Associate Ppt | Databricks-Certified-Data-Engineer-Associate Exam PDF

Valid Braindumps Databricks-Certified-Data-Engineer-Associate Ppt | Databricks-Certified-Data-Engineer-Associate Exam PDF

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Databricks Certified Data Engineer Associate certification is a highly sought-after certification in the data engineering industry. Databricks Certified Data Engineer Associate Exam certification demonstrates that a candidate has the knowledge and skills required to design and build data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is highly valued by employers in various industries.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a challenging and highly respected certification for data professionals. It is designed to test individuals' knowledge and skills in data engineering, with a focus on the Databricks platform. Individuals who pass the exam will have a valuable certification that can help them advance their careers and increase their earning potential.

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No doubt the Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate) certification is one of the most challenging certification exams in the market. This Databricks Databricks-Certified-Data-Engineer-Associate certification exam gives always a tough time to Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate) exam candidates. The PracticeDump understands this hurdle and offers recommended and real Databricks Databricks-Certified-Data-Engineer-Associate exam practice questions in three different formats.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

  • A. Silver tables contain aggregates while Bronze data is unaggregated.
  • B. Silver tables contain a more refined and cleaner view of data than Bronze tables.
  • C. Silver tables contain more data than Bronze tables.
  • D. Silver tables contain less data than Bronze tables.
  • E. Silver tables contain a less refined, less clean view of data than Bronze data.

Answer: B

Explanation:
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?


NEW QUESTION # 95
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

  • A. spark.delta.sql
  • B. spark.sql
  • C. dbutils.sql
  • D. spark.table
  • E. spark.delta.table

Answer: B


NEW QUESTION # 96
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

  • A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
  • B. Records that violate the expectation cause the job to fail.
  • C. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
  • D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Answer: A

Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
Reference:
Delta Live Tables Expectations
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 97
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

  • A. Databricks Repos automatically saves development progress
  • B. Databricks Repos allows users to revert to previous versions of a notebook
  • C. Databricks Repos provides the ability to comment on specific changes
  • D. Databricks Repos supports the use of multiple branches
  • E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Answer: D

Explanation:
Explanation
An advantage of using Databricks Repos over the built-in Databricks Notebooks versioning is the ability to work with multiple branches. Branching is a fundamental feature ofversion control systems like Git, which Databricks Repos is built upon. It allows you to create separate branches for different tasks, features, or experiments within your project. This separation helps in parallel development and experimentation without affecting the main branch or the work of other team members. Branching provides a more organized and collaborative development environment, making it easier to merge changes and manage different development efforts. While Databricks Notebooks versioning also allows you to track versions of notebooks, it may not provide the same level of flexibility and collaboration as branching in Databricks Repos.


NEW QUESTION # 98
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 format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
  • B. The PARQUET file format does not support COPY INTO.
  • C. The names of the files to be copied were not included with the FILES keyword.
  • D. The COPY INTO statement requires the table to be refreshed to view the copied rows.
  • E. The previous day's file has already been copied into the table.

Answer: E

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 # 99
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