Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here’s why this is the best choice:

Retention Policy:

A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.

Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.

Locking the Retention Policy:

Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.

Steps to Implement:

Set the Retention Policy:

Define a retention period for the bucket to ensure that all objects are protected for the required duration.

Lock the Retention Policy:

Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.

Reference Links:

Cloud Storage Retention Policy Documentation

How to Set a Retention Policy

To optimize the performance of a complex Spark job on Dataproc that heavily relies on shuffling operations, and given the cost constraints of using preemptible VMs, switching from HDDs to SSDs and using HDFS as an intermediate storage layer can significantly improve performance. Here’s why option C is the best choice:

Performance of SSDs:

SSDs provide much faster read and write speeds compared to HDDs, which is crucial for performance-intensive operations like shuffling in Spark jobs.

Using SSDs can reduce I/O bottlenecks during the shuffle phase of your Spark job, improving overall job performance.

Intermediate Storage with HDFS:

Copying data from Google Cloud Storage (GCS) to HDFS for intermediate storage can reduce latency compared to reading directly from GCS.

HDFS provides better locality and faster data access within the Dataproc cluster, which can significantly improve the efficiency of shuffling and other I/O operations.

Cost Considerations:

Although SSDs are more expensive than HDDs, the performance improvement for shuffle-heavy workloads can justify the cost, especially if the improved performance reduces the overall runtime and thereby the cost of using preemptible VMs.

Using preemptible VMs with SSDs for this workload balances the cost and performance trade-offs effectively.

The core requirements are to protect sensitive data elements (data privacy) while retainingalldata for potential future use, and then using this preprocessed data for consumer analyses.

Retaining All Data:This immediately makes option B (remove sensitive fields) unsuitable because it involves data loss.

Protecting Sensitive Data for Analysis & Future Use:Masking is a de-identification technique that redacts or replaces sensitive data with a substitute, allowing the data structure and usability for analysis to be maintained without exposing the original sensitive values. This aligns with protecting data while still making it usable.

Cloud Data Loss Prevention (DLP) API:This service is specifically designed to discover, classify, and protect sensitive data. It offers various de-identification techniques, including masking.

Dataflow:This is a serverless, fast, and cost-effective service for unified stream and batch data processing. It's well-suited for transforming large datasets, such as those read from Cloud Storage, and can integrate with the DLP API for de-identification.

Writing to BigQuery:BigQuery is an ideal destination for an organization-wide dataset for consumer analyses.

Therefore, using Dataflow to read the data from Cloud Storage, leveraging the Cloud DLP API tomask(a form of de-identification) the sensitive elements, and then writing the processed (masked) data to BigQuery is the most appropriate solution. This approach protects privacy for the consumer analyses dataset while the original, unaltered data can still be retained in the restricted Cloud Storage bucket for future use cases that might require access to the original sensitive information (under strict governance).

Let's analyze why other options are less suitable:

Option B:"Remove sensitive fields" means data loss, which contradicts the requirement to retain all data for potential future use cases.

Option C:Encrypting sensitive fields with Cloud KMS and writing them to BigQuery is a valid way to protect data. However, for "consumer analyses," masked data is generally more directly usable than encrypted data. Analysts would typically work with de-identified (e.g., masked) data rather than directly querying encrypted fields and managing decryption keys for analytical purposes. While decryption is possible, masking often provides a better balance of privacy and utility for broad analysis. The question also implies creating a datasetforanalysis, where masking makes the data ready-to-use for that purpose. The original data remains in Cloud Storage.

Option D:Using CMEK encrypts the entire object in Cloud Storage at rest. While this protects the data in Cloud Storage, federated queries from BigQuery would access the raw, unmasked data (assuming decryption occurs seamlessly). This doesn't address the preprocessing requirement of protectingcertain sensitive data elementswithin the data itself for theconsumer analysesdataset. The goal is to create a de-identified dataset for analysis, not just secure the raw data at rest.

Dialogflow is a conversational AI platform that allows for easy implementation of chatbots without needing to code. It has built-in integration for both text and voice input via APIs like Cloud Speech-to-Text. Defining intents and entity types allows you to map common queries and keywords to responses. This would provide a low/no-code way to quickly build and iteratively improve the chatbot capabilities.

https://cloud.google.com/dialogflow/docs Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech.