Exam Professional-Data-Engineer Review, Professional-Data-Engineer Hot Spot Questions
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Professional-Data-Engineer Hot Spot Questions, Professional-Data-Engineer Pdf Version
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Google Certified Professional Data Engineer Exam Sample Questions (Q76-Q81):
NEW QUESTION # 76
Which of the following statements is NOT true regarding Bigtable access roles?
- A. To give a user access to only one table in a project, you must configure access through your application.
- B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
- C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
- D. You can configure access control only at the project level.
Answer: B
Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances.
Reference: https://cloud.google.com/bigtable/docs/access-control
NEW QUESTION # 77
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?
- A. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level
- B. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
- C. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
- D. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range
Answer: A
NEW QUESTION # 78
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create an embedding column
- B. Create a multi-hot column
- C. Create a hash bucket
- D. Create a one-hot column
Answer: A
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-dee
NEW QUESTION # 79
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. None of the above
- B. Only Batch Data Pipelines
- C. Both Batch and Streaming Data Pipelines
- D. Only Streaming Data Pipelines
Answer: C
Explanation:
Explanation
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 80
You currently have transactional data stored on-premises in a PostgreSQL database. To modernize your data environment, you want to run transactional workloads and support analytics needs with a single database. You need to move to Google Cloud without changing database management systems, and minimize cost and complexity. What should you do?
- A. Migrate your PostgreSQL database to Cloud SQL for PostgreSQL.
- B. Migrate your workloads to AlloyDB for PostgreSQL.
- C. Migrate to BigQuery to optimize analytics.
- D. Migrate and modernize your database with Cloud Spanner.
Answer: B
Explanation:
The key requirements are:
On-premises PostgreSQL database.
Run transactional workloads AND support analytics needs with a single database.
Move to Google Cloud without changing database management systems (i.e., remain PostgreSQL- compatible).
Minimize cost and complexity.
AlloyDB for PostgreSQL (Option A) is the best fit for these requirements.
PostgreSQL-Compatible: AlloyDB is fully PostgreSQL-compatible, meaning minimal to no application changes are required ("without changing database management systems").
Transactional and Analytical Workloads: AlloyDB is designed to handle demanding transactional workloads while also providing significantly faster analytical query performance compared to standard PostgreSQL. It achieves this through its intelligent, database-optimized storage layer and columnar engine integration. This addresses the "single database" for both needs.
Cost and Complexity: As a managed service, it reduces operational complexity. Its performance benefits for both OLTP and OLAP can lead to better cost-efficiency by handling mixed workloads effectively on a single system.
Let's analyze why other options are less suitable:
B (Migrate to BigQuery): BigQuery is an analytical data warehouse, not designed for transactional workloads.
This violates the "single database" for both types of workloads and "without changing database management systems" (as BigQuery is not PostgreSQL).
C (Migrate to Cloud Spanner): Cloud Spanner is a globally distributed, horizontally scalable relational database. While excellent for high-availability transactional workloads, it has its own SQL dialect (ANSI
2011 with extensions, not fully PostgreSQL wire-compatible without tools like PGAdapter, which adds complexity) and a different architecture. This would involve more significant changes than moving to a PostgreSQL-compatible system. The requirement was "without changing database management systems." D (Migrate to Cloud SQL for PostgreSQL): Cloud SQL for PostgreSQL is a fully managed PostgreSQL service. It's excellent for transactional workloads and simpler analytical queries. However, for more demanding analytical needs on the same database instance, AlloyDB is specifically optimized to provide superior performance due to its architectural enhancements (like the columnar engine). If the analytical needs are significant, AlloyDB offers a better converged experience. While Cloud SQL is PostgreSQL-compatible, AlloyDB is positioned for superior performance on mixed workloads.
Reference:
Google Cloud Documentation: AlloyDB for PostgreSQL > Overview. "AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service for your most demandingtransactional and analytical workloads... AlloyDB offers full PostgreSQL compatibility, so you can migrate your existing PostgreSQL applications with no code changes." Google Cloud Documentation: AlloyDB for PostgreSQL > Key benefits. Highlights include "Industry-leading performance: ...up to 100x faster analytical queries than standard PostgreSQL." and "Support for transactional and analytical workloads: AlloyDB is designed to efficiently handle both transactional and analytical queries, allowing you to use a single database for a wide range of applications."
NEW QUESTION # 81
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