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Google Professional-Data-Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals who work with data on Google Cloud Platform. Passing the exam is a crucial step for professionals who want to prove their expertise in designing, building, and maintaining data processing systems. Professional-Data-Engineer Exam assesses the candidate's ability to use GCP technologies, such as BigQuery, Cloud Dataflow, and Cloud Pub/Sub, to solve complex data challenges.
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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer Exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.
NEW QUESTION # 64
Your Cloud Storage data lake has raw, processed, and historical data in different buckets. Data older than two years is rarely accessed, and all data must be retained for no longer than seven years. You are concerned about rising storage costs. How should you control costs for the historical data bucket?
Answer: C
Explanation:
Object Lifecycle Management (OLM) is the correct and fully managed feature in Cloud Storage designed to automate transitions between storage classes and object deletion based on rules like object age, which directly addresses the scenario's requirements for cost control and retention policy.
* Cost Control and Rarely Accessed Data: The Archive storage class offers the lowest storage cost, which is ideal for data accessed less than once a year (data older than two years is "rarely accessed").
OLM can automate the move to this class.
* Retention Policy: OLM rules can include a Delete action based on a condition like object age, ensuring the "no longer than seven years" retention policy is enforced automatically.
* Correcting other options:
* A (Custom Script): This is a custom, unmanaged, and potentially complex solution that would require maintenance and incur Compute Engine costs. OLM is the native, fully managed feature.
* C (Autoclass): Autoclass automatically manages storage classes to optimize costs, but it generally defaults to Standard for new uploads and transitions data based on access patterns.
While it is a managed service, explicitly defining a rule with a deletion policy (after seven years) and the specific transition at two years is best achieved and guaranteed with an OLM rule.
* D (Replication): Replicating to a different region increases complexity and typically incurs data transfer and duplicate storage costs, which works against the goal of controlling rising storage costs.
Reference: Google Cloud Documentation on Object Lifecycle Management and Storage Classes:
"Object Lifecycle Management (OLM) provides a method for you to automate the process of managing your objects in Cloud Storage. To configure a lifecycle configuration, you supply a set of rules that apply to current and future objects in the bucket. When an object meets the conditions in a rule, Cloud Storage performs the specified action." (Source: Object Lifecycle Management)
"Archive storage is the lowest-cost, highly durable storage service for data archiving, online backup, and disaster recovery... Archive storage is the best choice for data that you plan to access less than once a year." (Source: Storage classes, confirming Archive is best for "rarely accessed" data at the lowest cost.) Lifecycle Rules can be configured to... Change an object's storage class (e.g., from Standard to Archive when age > 2 years) and Delete older object versions or the object itself (e.g., when age > 7 years). (Source:
Configuration examples for Object Lifecycle Management).
NEW QUESTION # 65
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?
Answer: D
NEW QUESTION # 66
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?
Answer: D
NEW QUESTION # 67
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?
Answer: D
Explanation:
https://cloud.google.com/dataproc/docs/concepts/compute/preemptible-vms
NEW QUESTION # 68
The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?
Answer: B
Explanation:
To resolve the concurrency issue in BigQuery caused by the introduction of hundreds of non-time-sensitive SQL pipelines, the best approach is to differentiate the types of queries based on their urgency and resource requirements. Here's why option C is the best choice:
SQL Pipelines as Batch Queries:
Batch queries in BigQuery are designed for non-time-sensitive operations. They run in a lower priority queue and do not consume slots immediately, which helps to reduce the overall slot consumption during peak times.
By converting non-time-sensitive SQL pipelines to batch queries, you can significantly alleviate the pressure on slot reservations.
Ad-Hoc Queries as Interactive Queries:
Interactive queries are prioritized to run immediately and are suitable for ad-hoc analysis where users expect quick results.
Running ad-hoc queries as interactive jobs ensures that analysts can get their results without delay, improving productivity and user satisfaction.
Concurrency Management:
This approach helps balance the workload by leveraging BigQuery's ability to handle different types of queries efficiently, reducing the likelihood of encountering quota errors due to slot exhaustion.
Steps to Implement:
Identify Non-Time-Sensitive Pipelines:
Review and identify SQL pipelines that are not time-critical and can be executed as batch jobs.
Update Pipelines to Batch Queries:
Modify these pipelines to run as batch queries. This can be done by setting the priority of the query job to BATCH.
Ensure Ad-Hoc Queries are Interactive:
Ensure that all ad-hoc queries are submitted as interactive jobs, allowing them to run with higher priority and immediate slot allocation.
Reference:
BigQuery Batch Queries
BigQuery Slot Allocation and Management
NEW QUESTION # 69
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