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To prepare for the exam, candidates can take advantage of various resources provided by Google, such as online training courses, practice exams, and study guides. In addition, candidates can gain hands-on experience with Google Cloud Platform by working on real-world projects and labs. With the increasing demand for data engineers and the growing popularity of cloud-based solutions, the Google Professional-Data-Engineer Certification can provide a significant boost to an individual's career prospects in the field of data engineering.
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To be eligible for the Google Professional-Data-Engineer exam, candidates are required to have a deep understanding of data processing technologies, such as Hadoop, Spark, and other big data frameworks. They should also be proficient in programming languages such as Python, Java, or Go, and have experience in designing and developing data processing pipelines. Additionally, candidates should have hands-on experience working with Google Cloud Platform services such as BigQuery, Dataflow, and Dataproc. Passing the Google Professional-Data-Engineer Exam can prove to be a valuable asset for data professionals who want to advance their careers or demonstrate their expertise in managing data solutions on Google Cloud.
NEW QUESTION # 58
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: A
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 # 59
You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?
Answer: A
NEW QUESTION # 60
Cloud Dataproc charges you only for what you really use with _____ billing.
Answer: C
Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
Reference: https://cloud.google.com/dataproc/docs/concepts/overview
NEW QUESTION # 61
You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?
Answer: B
Explanation:
To implement for-user crypto-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
Customer-Managed Encryption Keys (CMEK):
CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
For-User Crypto-Deletion:
For-user crypto-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
Native Integration:
Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
Create a CMEK in Cloud KMS:
Set up a new customer-managed encryption key in Cloud KMS.
Associate the CMEK with BigQuery Tables:
When creating a new table in BigQuery, specify the CMEK to be used for encryption.
This can be done through the BigQuery console, CLI, or API.
Reference:
BigQuery and CMEK
Cloud KMS Documentation
Encrypting Data in BigQuery
NEW QUESTION # 62
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?
Answer: B
Explanation:
Explanation
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io
NEW QUESTION # 63
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