Google Professional Data Engineer Exam Practice Questions (P. 2)

GPT-4o - Answer

For securing client data in a multi-client setup using Google BigQuery, the ideal strategy includes segmenting each client's data into separate datasets, restricting access to these datasets to only approved users or groups, and utilizing specific IAM roles to manage permissions finely. This approach ensures data isolation while providing tailored access based on individual client requirements, thereby securing and optimizing data access management within a diverse client environment. This method effectively prevents clients from accessing each other’s data, aligning with best practices for data security and privacy on cloud platforms.

GPT-4o - Answer

Opting for Cloud SQL as the best choice for a point-of-sale application is primarily due to its ability to handle complex transactions integral to payment processing. This is crucial in environments that require not only atomic transactions but also more comprehensive operations involving updates to multiple related data items, like inventory and sales information. Moreover, while Cloud SQL does require some manual scaling regarding computation, it supports automatic scaling in storage capacity, which is a significant advantage for managing potentially exponential increases in data without needing extensive manual infrastructure management.

GPT-4o - Answer

While the option indicating future mutations having different features from mutated samples already in the database seems inconsistent with traditional anomaly detection, where anomalies are detected based on deviations from a "normal" pattern, it’s crucial to remember the context of anomaly detection in rapidly evolving datasets like mutations. It can be reasoned that anticipating novel characteristics in mutations aligns with the unsupervised nature of anomaly detection, which often must adapt to new, unseen anomalies. However, there is a strong argument made that future mutations will likely resemble existing anomalies, making option D also a practical choice in this scenario.

GPT-4o - Answer

To mitigate the consistency issues observed with streaming inserts in BigQuery when running subsequent aggregative queries, the optimal approach is to understand and estimate the average latency time it takes for streamed data to be written to storage. By determining this latency, you can schedule your queries to execute after a delay that is twice the average latency period. This allows sufficient time for all the in-flight data to be securely written and available for querying, thus, reducing the likelihood of missing or incomplete data in the generated reports. Although this might seem like an estimation rather than a technical fix, it efficiently addresses the issue within the constraints of near real-time data processing requirements without needing significant architectural changes. This method is most apt for maintaining near real-time analysis while ensuring data completeness.

GPT-4o - Answer

To effectively secure your data warehouse in Google BigQuery, the initial step is to audit data access, which is best accomplished using Google Stackdriver Audit Logs. This tool allows you to track who accesses your data, when, and what operations they perform, providing essential insights for identifying current usage patterns and potential security vulnerabilities. This foundational knowledge is crucial before implementing more targeted access controls. Note, while auditing IAM policies per table provides detailed access control, it usually follows an audit to pinpoint specific access needs.

GPT-4o - Answer

Choosing Cloud Dataproc with the Google Cloud Storage connector for migrating a Hadoop cluster to the cloud, as suggested in option D, aligns perfectly with the need to reuse existing Hadoop jobs while minimizing management overhead. Dataproc, being a fully managed service, helps seamlessly run Hadoop and Spark jobs. The use of Google Cloud Storage through its connector not only provides persistent storage for data beyond the lifespan of the cluster but also offers scalability and cost-efficiency over using persistent disks. This makes it an optimal solution for cloud migrations, ensuring that your data remains secure and easily accessible even after cluster termination.

GPT-4o - Answer

For the provided data on bank transactions, unsupervised learning (Option B) can effectively identify potential fraud activities by detecting unusual patterns without pre-labeled outcomes. Additionally, clustering (Option C) is applicable as it can group transactions based on similarities in features such as amount or location, providing valuable insights into transaction categories. The choice of reinforcement learning (Option E) to predict transaction locations, although unconventional, might explore optimizing policies for transaction routing or fraud prevention if understood as part of a dynamic system with rewards based systems. Hence, the chosen applications are B, C, and E.

GPT-4o - Answer

Opting for Google Cloud Storage, as indicated in option A, aligns well with Google Cloud Dataproc's architecture, which is engineered to integrate seamlessly with GCS. This approach not only reduces costs associated with large persistent disk requirements but also enhances durability and flexibility by allowing data sharing across various clusters. Moreover, it addresses the CIO's concern about the expense linked to block storage, thereby presenting a solution that is both economically and technically advantageous.