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Google Professional-Data-Engineer Certification is a highly sought-after certification that is designed for data professionals who are interested in demonstrating their proficiency in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for data engineers, data analysts, and data scientists who have experience working with data processing systems and are looking to advance their careers.
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Google Certified Professional Data Engineer Exam Sample Questions (Q190-Q195):
NEW QUESTION # 190
The Dataflow SDKs have been recently transitioned into which Apache service?
Answer: B
Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/
NEW QUESTION # 191
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
Answer: A
Explanation:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_date- sharded_tables_into_ingestion-time_partitioned_tables
NEW QUESTION # 192
You are planning to use Cloud Storage as pad of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?
Answer: D
Explanation:
To minimize the cost of storing and retrieving objects in a Cloud Storage bucket while ensuring that cost optimization efforts are transparent to the users and applications, enabling Autoclass is the best approach.
Here's why:
* Autoclass Feature:
* Autoclassautomatically transitions objects between different storage classes (Standard, Nearline, Coldline, and Archive) based on their access patterns.
* It ensures that frequently accessed data is kept in lower-latency, higher-cost storage classes and infrequently accessed data is moved to higher-latency, lower-cost storage classes.
* Cost Optimization:
* Autoclass optimizes storage costs by automatically moving objects to the most cost-effective storage class based on actual usage patterns, without manual intervention.
* This feature ensures that objects are stored in the most economical class appropriate for their access frequency, reducing storage costs over time.
* Transparency to Users:
* The transition of objects between storage classes is handled automatically by Cloud Storage, making the process transparent to users and applications.
* Users and applications interact with the objects in the same way, regardless of the underlying storage class, ensuring seamless access.
Steps to Implement:
* Create a Cloud Storage Bucket:
* When creating a new Cloud Storage bucket, enable the Autoclass feature.
* Configure Autoclass:
* Autoclass configuration is typically a straightforward process in the Google Cloud Console, where you enable it during bucket creation.
* Monitor and Adjust:
* Monitor the storage and access patterns through the Google Cloud Console to ensure that Autoclass is optimizing costs as expected.
Reference Links:
* Google Cloud Storage Autoclass
* Optimizing Storage Costs with Autoclass
NEW QUESTION # 193
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?
Answer: B
NEW QUESTION # 194
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a
machine-learning process. You want to support a logistic regression model. You also need to monitor and
adjust for null values, which must remain real-valued and cannot be removed. What should you do?
Answer: D
NEW QUESTION # 195
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