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The GAQM Databricks-Certified-Data-Engineer-Associate Exam is a certification exam designed for data engineers who want to prove their proficiency in designing and building data pipelines using Databricks. The Databricks Certified Data Engineer Associate certification is highly valued in the data engineering industry and is recognized globally. Databricks Certified Data Engineer Associate Exam certification covers various topics related to data engineering, big data processing and analytics, and cloud computing using Databricks. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers who work with Databricks and want to advance their careers in data engineering.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which location can the data engineer review their permissions on the table?

A. Repos
B. Jobs
C. Dashboards
D. Catalog Explorer

Answer: D

NEW QUESTION # 54
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. Data Explorer
B. Delta Lake
C. Unity Catalog
D. Auto Loader
E. Databricks SQL

Answer: D

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 55
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 previous day's file has already been copied into the table.
B. The PARQUET file format does not support COPY INTO.
C. The COPY INTO statement requires the table to be refreshed to view the copied rows.
D. The names of the files to be copied were not included with the FILES keyword.
E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

Answer: A

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. Reference: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

NEW QUESTION # 56
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

A. pyspark.sql.types.DateType
B. Cron syntax
C. There is no way to represent and submit this information programmatically
D. datetime
E. pyspark.sql.types.TimestampType

Answer: B

Explanation:
Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. Reference: Schedule a job, Jobs API, Databricks CLI, Cron expressions

NEW QUESTION # 57
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project's release, the managerwants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
B. They can set the query's refresh schedule to end on a certain date in the query scheduler.
C. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
D. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
E. They can set the query's refresh schedule to end after a certain number of refreshes.

Answer: B

NEW QUESTION # 58
......

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