Best Quality Databricks-Certified-Data-Engineer-Associate Exam Questions Databricks Test To Gain Brilliante Result!
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Databricks Certified Data Engineer Associate certification exam covers topics such as data engineering concepts, data ingestion, data processing, data storage, and data transformation using Apache Spark and Delta Lake. Candidates who pass Databricks-Certified-Data-Engineer-Associate exam will have a deep understanding of the Databricks platform and will be able to design, build, and maintain data pipelines that are scalable, reliable, and efficient. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data analysts, and data scientists who work with big data and want to enhance their skills and advance their careers.
NEW QUESTION # 37
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
- A. When they are manually running reports with a large amount of data
- B. When they are working with SQL within Databricks SQL
- C. When they are running automated reports to be refreshed as quickly as possible
- D. When they are concerned about the ability to automatically scale with larger data
- E. When they are working interactively with a small amount of data
Answer: E
Explanation:
Explanation
A Single Node cluster is a cluster consisting of an Apache Spark driver and no Spark workers. A Single Node cluster supports Spark jobs and all Spark data sources, including Delta Lake. A Standard cluster requires a minimum of one Spark worker to run Spark jobs.
NEW QUESTION # 38
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 1 day from the query's page in Databricks SQL.
- B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
- C. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
- D. They can schedule the query to run every 12 hours from the Jobs UI.
- E. They can schedule the query to run every 1 day from the Jobs UI.
Answer: A
NEW QUESTION # 39
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?
- A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
- B. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
- C. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
- D. There is no way to determine why a Job task is running slowly.
- E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
Answer: C
NEW QUESTION # 40
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?
- A. More information is needed to determine the correct response
- B. dbfs:/user/hive/database/customer360
- C. dbfs:/user/hive/warehouse
- D. dbfs:/user/hive/customer360
Answer: C
Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location
NEW QUESTION # 41
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?
- A. The pipeline will need to stop using the medallion-based multi-hop architecture
- B. The pipeline will need to be written entirely in SQL
- C. None of these changes will need to be made
- D. The pipeline will need to be written entirely in Python
- E. The pipeline will need to use a batch source in place of a streaming source
Answer: C
NEW QUESTION # 42
In which of the following file formats is data from Delta Lake tables primarily stored?
- A. Parquet
- B. JSON
- C. Delta
- D. A proprietary, optimized format specific to Databricks
- E. CSV
Answer: A
Explanation:
Explanation
https://docs.delta.io/latest/delta-faq.html
NEW QUESTION # 43
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 PARQUET file format does not support COPY INTO.
- D. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
- E. The previous day's file has already been copied into the table.
Answer: E
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 # 44
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?
- A. They can clone the existing task in the existing Job and update it to run the new notebook.
- B. They can create a new task in the existing Job and then add it as a dependency of the original task.
- C. They can create a new job from scratch and add both tasks to run concurrently.
- D. They can clone the existing task to a new Job and then edit it to run the new notebook.
- E. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
Answer: D
NEW QUESTION # 45
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?
- A. Parquet files have a well-defined schema
- B. Parquet files will become Delta tables
- C. Parquet files have the ability to be optimized
- D. Parquet files can be partitioned
- E. CREATE TABLE AS SELECT statements cannot be used on files
Answer: A
Explanation:
Explanation
https://www.databricks.com/glossary/what-is-parquet#:~:text=Columnar%20storage%20like%20Apache%20Par Columnar storage like Apache Parquet is designed to bring efficiency compared to row-based files like CSV.
When querying, columnar storage you can skip over the non-relevant data very quickly. As a result, aggregation queries are less time-consuming compared to row-oriented databases.
NEW QUESTION # 46
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 turn on the Auto Stop feature for the SQL endpoint.
- B. They can increase the cluster size of the SQL endpoint.
- C. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - D. They can turn on the Serverless feature for the SQL endpoint.
- E. They can increase the maximum bound of the SQL endpoint's scaling range
Answer: B
Explanation:
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html
NEW QUESTION # 47
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
- A. Unity Catalog
- B. Delta Lake
- C. Data Explorer
- D. Databricks SQL
- E. Auto Loader
Answer: E
NEW QUESTION # 48
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
- A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
- B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
- C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
- D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
- E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
Answer: D
Explanation:
Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.
NEW QUESTION # 49
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?
- A. They can turn on the Auto Stop feature for the SQL endpoint.
- B. They can increase the maximum bound of the SQL endpoint's scaling range.
- C. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - D. They can increase the cluster size of the SQL endpoint.
- E. They can turn on the Serverless feature for the SQL endpoint.
Answer: B
NEW QUESTION # 50
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. USING DELTA
- B. USING CSV
- C. FROM CSV
- D. FROM "path/to/csv"
- E. None of these lines of code are needed to successfully complete the task
Answer: B
NEW QUESTION # 51
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?
- A. It is not possible to use SQL in a Python notebook
- B. They can simply write SQL syntax in the cell
- C. They can attach the cell to a SQL endpoint rather than a Databricks cluster
- D. They can add %sql to the first line of the cell
- E. They can change the default language of the notebook to SQL
Answer: D
NEW QUESTION # 52
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 loaded into a quarantine table.
- B. Records that violate the expectation cause the job to fail.
- C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
- D. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
- E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Answer: B
Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/expectations.html
Action
Result
warn (default)
Invalid records are written to the target; failure is reported as a metric for the dataset.
drop
Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset.
fail
Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.
NEW QUESTION # 53
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 D
- B. Option A
- C. Option E
- D. Option B
- E. Option C
Answer: C
NEW QUESTION # 54
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain raw data with a schema applied.
- B. Bronze tables contain a less refined view of data than raw data.
- C. Bronze tables contain aggregates while raw data is unaggregated.
- D. Bronze tables contain more truthful data than raw data.
- E. Bronze tables contain less data than raw data files.
Answer: C
NEW QUESTION # 55
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Earning the Databricks-Certified-Data-Engineer-Associate certification can help individuals advance their careers in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification is recognized globally and can demonstrate to employers that the individual has the skills and knowledge required to work with Databricks effectively. It can also lead to better job opportunities and higher salaries.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive examination that tests the skills of professionals who work with data on the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is designed for individuals who are responsible for designing, building, and maintaining data pipelines, as well as data analysts, data architects, and data engineers. The Databricks Certified Data Engineer Associate certification is recognized globally and validates the skills and knowledge of individuals in the field of data engineering.
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