PDE Practice Test 34 — 15 Questions

Question 1

Which three of the following are valid BigQuery data loading methods? (Choose THREE.)

Accepted Answer

Data Transfer Service from Amazon S3. Option A is correct because BigQuery Data Transfer Service natively supports scheduled, managed transfers from Amazon S3 into BigQuery, making it a valid loading method. Option D is correct because batch loading from Cloud Storage is one of the primary and most common ways to load data into BigQuery, using load jobs that read from GCS URIs. Option E is correct because streaming inserts via the legacy streaming API (tabledata.insertAll) are a supported, documented method for loading data into BigQuery in real time. Option B is not a valid loading method because Cloud SQL is a managed relational database service and does not write directly into BigQuery; data would need to be exported and then loaded via a supported path. Option C is not valid because Cloud Dataproc is a managed Spark/Hadoop service, and there is no direct file upload mechanism from Dataproc into BigQuery; Dataproc would typically write to Cloud Storage, which is then loaded into BigQuery.

Answer

Using Cloud SQL to write to BigQuery

Answer

Direct file upload via Cloud Dataproc

Question 2

A company needs to store logs in Cloud Storage for compliance, with a requirement that logs cannot be deleted or overwritten for a period of 7 years. Which Cloud Storage feature should they enable?

Accepted Answer

Bucket Lock with a retention policy of 7 years.. Bucket Lock with a retention policy of 7 years is the correct feature because it enforces a WORM (Write Once, Read Many) model on the bucket. Once a retention policy is locked, objects cannot be deleted or overwritten until the retention period expires, meeting the compliance requirement for immutable log storage.

Answer

Requester Pays bucket setting.

Answer

Versioning enabled with Object Hold.

Answer

Object lifecycle management with a delete rule after 7 years.

Question 3

A data engineer is designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery using the Storage Write API in exactly-once mode. The pipeline must handle late-arriving data (up to 1 hour) and maintain correct aggregation results. Which trigger configuration should they use?

Accepted Answer

After watermark trigger with allowed lateness of 1 hour. The After watermark trigger with allowed lateness of 1 hour is the correct choice because it emits results once the watermark passes the end of the window, while still accepting late-arriving events up to 1 hour past the watermark and including them in the correct event-time window. This aligns with the requirement to handle up to 1 hour of late data and maintain correct aggregation results. Note that exactly-once delivery to BigQuery is provided by the Storage Write API sink itself, not by the trigger; the trigger choice affects completeness and correctness of the aggregation, not the exactly-once guarantee.

Answer

Default trigger without allowed lateness

Answer

After count trigger with count of 1000

Answer

Fixed time trigger every 5 minutes

Question 4

A data engineer is designing a batch processing pipeline that runs daily. The pipeline reads CSV files from GCS, transforms them using Python, and writes the results to BigQuery. They need to parameterize the pipeline for different environments and run it on a schedule. Which THREE components should they use? (Choose 3)

Accepted Answer

Cloud Composer. Cloud Composer (A) is correct because it is a managed Apache Airflow service that provides DAG-based scheduling and parameterization, making it ideal for orchestrating a daily batch pipeline with environment-specific variables. Dataproc (B) is correct because it offers managed Spark/Hadoop clusters that can run Python-based transformations over the CSV data read from GCS at batch scale. Dataflow Flex Template (C) is correct because it packages a Dataflow pipeline (including Python transforms) into a reusable, parameterized template that can be invoked with different runtime parameters per environment. Storage Transfer Service (D) is not appropriate here because it only moves data between storage systems and performs no transformation or scheduling logic. Cloud Functions (E) is unsuitable because it is an event-driven, short-lived serverless function, not designed for orchestrating daily batch pipelines or large-scale data transformation.

Answer

Storage Transfer Service

Answer

Cloud Functions

Question 5

A startup is using Cloud Build to automate the training and deployment of their machine learning models. The workflow is defined in cloudbuild.yaml and includes steps to: 1) Run a training job on AI Platform Training, 2) Build a custom prediction container, 3) Deploy the container to Cloud Run for serving. The deployment step fails intermittently with the error: 'Cloud Run service already exists and is not owned by the calling user.' You need to fix this so that deployments are reliable. What should you do?

Accepted Answer

Ensure the Cloud Build service account has the 'run.services.update' permission on the Cloud Run service.. The error indicates that the Cloud Run service already exists and the Cloud Build service account does not own it. The Cloud Build service account needs the 'run.services.update' IAM permission on the specific Cloud Run service to modify it during deployment. Granting this permission allows the service account to update the existing service reliably, resolving the intermittent failure.

Answer

Delete the existing Cloud Run service manually before each build.

Answer

Use 'gcloud run deploy --replace' in the build step to force replace the existing service.

Answer

Use Cloud Run for Anthos instead of fully managed Cloud Run to avoid ownership issues.

Question 6

An e-commerce company uses Cloud Spanner for order processing. They need to query orders by customer ID and retrieve all order items. Which schema design pattern should they use for optimal performance?

Accepted Answer

Denormalize by storing order items as a repeated field in the orders table.. For this access pattern, the optimal Spanner design is to denormalize order items as a repeated field in the Orders table. A repeated field stores child rows inline with the parent row, so a query by customer_id (with a secondary index on customer_id) retrieves the order and all its items in a single lookup without a join or extra network round-trip. Interleaved tables co-locate child rows with the parent, but they only help when the query uses the parent's primary key prefix; querying by customer_id would still require a secondary index and then a join-like lookup to fetch interleaved children, so option A is not the best fit here.

Answer

Use interleaved tables where Orders is the parent and OrderItems is an interleaved child table with the same primary key prefix.

Answer

Store all data in a single table with nullable columns for order item attributes.

Answer

Create two separate tables with a secondary index on customer_id in the orders table and a secondary index on order_id in the order_items table.

Question 7

A company needs to stream real-time user activity data from their application into BigQuery for immediate dashboarding. They want to minimize latency (under 5 seconds) and ensure exactly-once delivery. Which TWO options should they consider? (Choose 2)

Accepted Answer

Use BigQuery Storage Write API in committed mode. Option B is correct because the BigQuery Storage Write API in committed mode provides exactly-once semantics via write streams and offsets, and it supports low-latency streaming ingestion suitable for sub-5-second dashboarding. Option E is correct because Pub/Sub plus Dataflow is the canonical Google Cloud streaming pipeline: Pub/Sub ingests events durably, and Dataflow's BigQueryIO in STREAMING mode with exactly-once processing guarantees deduplication and exactly-once writes to BigQuery. Option A is not ideal because Cloud Functions invoking the BigQuery REST API (tabledata.insertAll) does not provide exactly-once delivery and adds per-event overhead and cold-start latency. Option C is wrong because legacy streaming inserts offer at-least-once semantics and can produce duplicate rows, violating the exactly-once requirement. Option D is not the best fit because running Kafka on Dataproc adds operational complexity, and the Kafka connector typically relies on the Storage Write API or streaming inserts without inherently guaranteeing exactly-once end-to-end delivery in this scenario.

Answer

Use Cloud Functions to receive events and call the BigQuery REST API

Answer

Use BigQuery legacy streaming inserts directly from the application

Answer

Use Apache Kafka on Dataproc and write to BigQuery via the BigQuery Kafka connector

Question 8

A global e-commerce platform requires a relational database that can handle millions of transactions per second across regions with strong consistency and automatic failover. The database must also support SQL joins. Which database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the correct choice because it is a globally distributed, horizontally scalable relational database that provides strong consistency and automatic failover across regions, while fully supporting SQL joins. It combines the benefits of a traditional relational database with the horizontal scalability of NoSQL systems, making it ideal for high-throughput, globally distributed applications requiring ACID transactions.

Answer

Cloud SQL

Answer

Cloud Firestore

Answer

Cloud Bigtable

Question 9

A company wants to store backups of on-premises databases in Google Cloud for long-term retention. They need WORM (Write Once, Read Many) compliance and object-level retention policies. What should they use?

Accepted Answer

Cloud Storage with retention policies. Cloud Storage supports WORM (Write Once, Read Many) compliance through bucket retention policies and object retention (object-level retention lock), along with legal holds. A bucket retention policy sets a minimum retention period for all objects in the bucket, while object retention allows you to set or extend retention on individual objects, and legal holds prevent deletion or modification until removed. Together these provide the object-level retention controls required for long-term backup retention and regulatory compliance, making Cloud Storage with retention policies the correct choice.

Answer

Firestore backups

Answer

Cloud Storage with Object Lock

Answer

BigQuery table snapshots

Question 10

A data engineer needs to load a 10 GB CSV file from GCS into BigQuery. The file contains some malformed rows that should be skipped. Which approach is most efficient?

Accepted Answer

Use the bq command-line tool with the --max_bad_records flag. The `bq` command-line tool's `--max_bad_records` flag allows BigQuery's native CSV loader to skip malformed rows up to a specified limit during a load job. This is the most efficient approach for a one-time batch load of a 10 GB file, as it avoids the overhead of spinning up separate processing clusters (Dataproc, Dataflow) or streaming each row individually, leveraging BigQuery's optimized ingestion pipeline.

Answer

Use Dataproc to run a Spark job that cleans the data and writes to BigQuery

Answer

Use the Storage Write API to stream each row, skipping bad ones in code

Answer

Use a Dataflow pipeline to read CSV, filter bad rows, and write to BigQuery

Question 11

Your company is migrating an on-premises Hadoop cluster to Google Cloud. You need to transform large datasets using Spark SQL. Which Google Cloud service should you use?

Accepted Answer

Dataproc. Dataproc is the managed Spark and Hadoop service on Google Cloud, purpose-built for running existing Spark SQL workloads with minimal changes. It allows you to spin up a cluster, run your Spark SQL transformations on large datasets stored in Cloud Storage or BigQuery, and then tear it down, making it the direct equivalent of an on-premises Hadoop cluster in the cloud.

Answer

Dataflow

Answer

BigQuery

Answer

Cloud Dataprep

Question 12

A company needs to load data from a MySQL database into BigQuery daily. The data volume is 10 GB per day and the schema changes occasionally. They want to minimize costs and operational overhead. What is the MOST appropriate approach?

Accepted Answer

Export MySQL data to CSV, upload to GCS, and use BigQuery load jobs. For a daily batch load of 10 GB with occasional schema changes, the most cost-effective and low-overhead approach is to export the MySQL data to CSV or Parquet, upload it to Cloud Storage, and use BigQuery load jobs. BigQuery load jobs are free (you only pay for storage and queries), and they can handle schema changes via schema autodetect or by updating the table schema. Datastream is designed for continuous change data capture with low latency, which is unnecessary and likely more expensive for a once-daily batch load. BigQuery Data Transfer Service does not support MySQL as a source, and Cloud SQL federated queries are for querying external data, not for loading it into BigQuery.

Answer

Use Datastream to stream changes from MySQL to BigQuery

Answer

Use BigQuery Data Transfer Service for MySQL

Answer

Use Cloud SQL federated query from BigQuery

Question 13

A company wants to run complex analytical queries on structured data without managing infrastructure. The data volume is terabytes and queries can take seconds to minutes. Which service is appropriate?

Accepted Answer

BigQuery. BigQuery is correct because it is a serverless, highly scalable data warehouse designed for running complex analytical queries on terabytes of data with fast query performance (seconds to minutes) without any infrastructure management. It uses a columnar storage format and a distributed query engine to handle large-scale structured data efficiently, making it ideal for this use case.

Answer

Firestore

Answer

Cloud Bigtable

Answer

Cloud SQL

Question 14

Which Google Cloud database offers global distribution, strong consistency, and a 99.999% SLA?

Accepted Answer

Cloud Spanner. Cloud Spanner is the only Google Cloud database that provides global distribution (horizontally scaling across regions), strong consistency (external consistency with TrueTime), and a 99.999% SLA. It combines the benefits of relational database structure with non-relational horizontal scale, making it ideal for globally distributed, strongly consistent workloads.

Answer

Cloud Bigtable

Answer

Firestore

Answer

Cloud SQL

Question 15

A company has deployed a machine learning model on Vertex AI Prediction that serves real-time predictions for a customer-facing application. The model was trained using a custom container and is hosted on a single endpoint with a minimum number of nodes. Recently, the team noticed that during peak traffic, prediction latency increases significantly and some requests time out. The endpoint is configured with a baseline traffic split of 100% on the current model version. Which action should the team take to reduce latency and improve reliability?

Accepted Answer

Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target.. Configuring horizontal autoscaling with a higher maximum number of nodes and a CPU utilization target allows Vertex AI Prediction to automatically add more nodes during peak traffic, distributing the inference load and reducing latency. This directly addresses the root cause—insufficient compute resources under high demand—without requiring architectural changes or sacrificing availability.

Answer

Reduce the minimum number of nodes to zero to allow scale-to-zero during low traffic.

Answer

Place a Google Cloud Load Balancer in front of the Vertex AI endpoint to distribute requests across multiple endpoints.

Answer

Implement A/B testing by splitting traffic between two model versions to distribute load.