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Google Certified Professional Data Engineer Exam Sample Questions (Q331-Q336):

NEW QUESTION # 331
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use the WHERE_PARTITIONTIME pseudo column
B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D. Use the TABLE_DATE_RANGE function

Answer: D

Explanation:
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.

NEW QUESTION # 332
Which of these statements about exporting data from BigQuery is false?

A. The only supported export destination is Google Cloud Storage.
B. Data can only be exported in JSON or Avro format.
C. The only compression option available is GZIP.
D. To export more than 1 GB of data, you need to put a wildcard in the destination filename.

Answer: B

Explanation:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
https://cloud.google.com/bigquery/docs/exporting-data

NEW QUESTION # 333
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 workloads to AlloyDB for PostgreSQL.
B. Migrate to BigQuery to optimize analytics.
C. Migrate and modernize your database with Cloud Spanner.
D. Migrate your PostgreSQL database to Cloud SQL for PostgreSQL.

Answer: A

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 # 334
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

A. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
C. Put the data into Google Cloud Storage.
D. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Answer: B

NEW QUESTION # 335
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost- sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

A. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
B. Increase the size of your parquet files to ensure them to be 1 GB minimum.
C. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

Answer: C

NEW QUESTION # 336
......

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