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Preparation for the Databricks Certified Data Engineer Associate certification exam requires a solid understanding of data engineering concepts and experience working with Databricks. GAQM offers a range of study materials to help candidates prepare for the exam, including online courses, practice tests, and study guides. Candidates should also have hands-on experience working with Databricks to build and maintain data pipelines in order to be fully prepared for the exam.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam designed for data engineers who are interested in validating their skills and knowledge in working with Databricks. Databricks is a cloud-based data processing and analytics platform that is widely used by organizations to manage and analyze large datasets. Databricks Certified Data Engineer Associate Exam certification exam is designed to test the candidate's ability to design, build and maintain data pipelines using Databricks.
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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.
NEW QUESTION # 117
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
Answer: C
Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:
customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.
NEW QUESTION # 118
Which of the following commands will return the number of null values in the member_id column?
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 # 119
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?
Answer: E
Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation
NEW QUESTION # 120
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT.
Which code snippet should the data engineer use?
Answer: C
NEW QUESTION # 121
A data engineer is setting up a new Databricks pipeline that ingests clickstream events from Kafka and daily product catalogs from cloud object storage. To ensure auditability and easy reprocessing, the engineer wants to land all source data first. Later stages will handle cleaning, deduplication, and business modeling before the data is used in dashboards.
Which approach aligns with Medallion Architecture principles?
Answer: A
Explanation:
Databricks describes the Bronze layer in the Medallion Architecture as the raw ingestion layer , where source data is landed with minimal validation or transformation so that the original records are preserved for auditability and reprocessing. This is especially important for sources like Kafka clickstream events and file-based product catalogs, because downstream logic such as cleansing, deduplication, conformance, and business modeling should happen later in Silver and Gold layers. Bronze data is commonly stored in an append-only pattern and may include basic ingestion metadata, but it should not be heavily transformed at this stage. Databricks explicitly recommends limiting cleanup and validation in Bronze and using Silver for validation and deduplication. That makes option C the best match to Databricks guidance. Options A, B, and D all skip or misuse the Bronze layer, which reduces traceability and makes recovery or replay harder if business logic changes later.
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NEW QUESTION # 122
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