PDE Practice Test 45 — 15 Questions

Question 1

Which BigQuery feature allows you to query data directly from Cloud Storage without loading it into BigQuery storage?

Accepted Answer

External tables. BigQuery Omni is for multi-cloud, not external tables. External tables allow querying data in GCS.

Answer

BigQuery Omni

Answer

BigQuery ML

Answer

Federated queries

Question 2

A company uses Looker Studio to build dashboards from BigQuery data. They notice that queries take several seconds to return. They want to improve performance without changing the schema or adding materialized views. Which option should they use?

Accepted Answer

Enable BigQuery BI Engine on the relevant project.. BI Engine accelerates sub-second query response times in Looker Studio by caching data in memory within the BigQuery region.

Answer

Move the data to Cloud SQL.

Answer

Switch to BigQuery Omni for cross-cloud queries.

Answer

Use APPROX_COUNT_DISTINCT to speed up distinct counts.

Question 3

A data engineer needs to share a large BigQuery table with a different team, but wants to minimize storage costs. The table is 1 TB in size and is updated daily. The other team only needs read access to the data as of a specific point in time (e.g., end of each day). Which BigQuery feature should be used to provide a read-only copy without duplicating the entire table?

Accepted Answer

Table snapshot. BigQuery table clones provide a lightweight, writable copy of a table that initially shares storage with the base table and only incurs costs for changes made to the clone. Snapshots are read-only and also share storage but require the base table to be preserved until snapshot expires. For read-only point-in-time access, a snapshot is more appropriate because it is immutable and cost-effective.

Answer

Table clone

Answer

Authorized views

Answer

Time travel using FOR SYSTEM_TIME AS OF

Question 4

An organization is implementing a data lake on Google Cloud using Cloud Storage. They need to process both batch and streaming data with a unified pipeline. The team has experience with Apache Beam. Which architecture should they use to minimize operational overhead?

Accepted Answer

Kappa architecture with Cloud Dataflow using the same pipeline for batch and streaming. Kappa architecture uses a single streaming pipeline for both batch and streaming, simplifying operations. Dataflow implements Beam and supports both modes.

Answer

Use Cloud Dataproc for batch and Cloud Dataflow for streaming

Answer

Lambda architecture with Cloud Dataflow for batch and Cloud Pub/Sub for streaming

Answer

Use Cloud Data Fusion for both batch and streaming

Question 5

A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

Accepted Answer

Configure auto-scaling with higher min and max instances. Configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.

Answer

Reduce the number of input features

Answer

Switch from online to batch prediction

Answer

Use a larger machine type for the model

Question 6

A data scientist is using Vertex AI to train a model and wants to ensure that the training code and environment are reproducible. Which approach should they take?

Accepted Answer

Use Vertex AI Training with a pre-built container and specify the exact version of the framework. Specifying the exact version of a pre-built container in Vertex AI Training ensures that the same framework version, dependencies, and runtime environment are used every time the training job runs. This eliminates variability from package updates or environment drift, which is essential for reproducibility in machine learning pipelines.

Answer

Use Jupyter notebooks on Vertex AI Workbench

Answer

Use custom containers with fixed tags

Answer

Use Cloud Build to train the model

Question 7

A company needs to process large files (100GB each) from Cloud Storage using Dataproc. They want to minimize job execution time. Which configuration is most appropriate?

Accepted Answer

Use a cluster with preemptible worker nodes and high-CPU machine types. Preemptible worker nodes are significantly cheaper than standard nodes, allowing you to scale out the cluster with many more workers for the same cost, which directly reduces job execution time for embarrassingly parallel data processing tasks. High-CPU machine types are ideal for compute-intensive Dataproc jobs like data transformation or machine learning, as they provide more vCPUs per core for parallel processing. This combination maximizes parallelism and minimizes wall-clock time for large-scale batch jobs.

Answer

Use a single-node cluster

Answer

Use HDFS for input data to avoid network latency

Answer

Use a cluster with many standard worker nodes

Question 8

Refer to the exhibit. A Dataflow pipeline is failing intermittently with the shown error. Which step should the team take to ensure data quality and prevent such errors?

Accepted Answer

Modify the pipeline to handle parsing failures by sending invalid records to a dead letter queue.. The error indicates that the pipeline is failing due to malformed or unparseable data. By sending invalid records to a dead letter queue (DLQ), the pipeline can continue processing valid data while capturing and isolating bad records for later analysis or reprocessing. This pattern is a standard data quality practice in Apache Beam and Dataflow, ensuring that transient or corrupt data does not cause pipeline failures.

Answer

Increase the number of workers to process the data faster.

Answer

Add a monitoring alert on the 'system_lag' metric.

Answer

Use a strongly typed schema for the PCollection and let Beam automatically reject malformed data.

Question 9

Which THREE steps are required to set up a continuous training pipeline on Google Cloud using Vertex AI?

Accepted Answer

Create a Vertex AI Pipeline to orchestrate data preprocessing, training, and model evaluation.. Vertex AI Pipelines provide a managed, repeatable, and scalable way to orchestrate the entire ML workflow, including data preprocessing, training, and model evaluation. This is essential for a continuous training pipeline, as it automates the sequence of steps and ensures consistency across runs.

Answer

Run training on a single Compute Engine VM with a cron job.

Answer

Manually upload the model to Vertex AI Model Registry after each training run.

Question 10

A team needs to orchestrate a complex ETL workflow that includes conditional branching (if new data arrives, run transformation A, else run transformation B), error handling, and coordination across multiple services. Which service should they use?

Accepted Answer

Cloud Composer (Apache Airflow). Cloud Composer (Apache Airflow) is the correct choice because it is designed for orchestrating complex, multi-step ETL workflows with conditional branching, error handling, and cross-service coordination. Airflow's directed acyclic graphs (DAGs) natively support conditional logic (e.g., BranchPythonOperator), retries, and dependency management across heterogeneous services, making it ideal for this use case.

Answer

Cloud Functions

Answer

Cloud Workflows

Answer

Cloud Scheduler

Question 11

A financial services company needs to explain predictions from a complex ensemble model for regulatory compliance. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Explainable AI. Vertex AI Explainable AI is the correct service because it provides feature attributions and other explainability techniques (e.g., Shapley value approximations, integrated gradients) that help interpret predictions from complex ensemble models. This is essential for regulatory compliance, where the company must demonstrate how input features influence each prediction, ensuring transparency and auditability.

Answer

Vertex AI Vizier

Answer

Vertex AI Feature Store

Answer

Vertex AI Prediction

Question 12

An organization wants to integrate BigQuery Omni to query data stored in AWS S3. They have set up the necessary connections. What is the primary benefit of using BigQuery Omni over simply copying the data to BigQuery?

Accepted Answer

Ability to use BigQuery ML models on data in S3 without moving data.. BigQuery Omni allows you to query data across clouds without moving it, providing a unified analytics experience. It reduces data egress costs and avoids duplication.

Answer

Automatic encryption of data at rest in S3.

Answer

Lower latency queries due to in-memory caching.

Answer

Support for real-time streaming inserts into S3.

Question 13

A financial services firm processes sensitive transactions using Cloud Dataflow. The pipeline reads from Pub/Sub, performs stateful processing (e.g., fraud detection), and writes to Cloud Spanner. Compliance requires exactly-once processing semantics. Which configuration ensures exactly-once processing?

Accepted Answer

Set Dataflow pipeline to exactly-once mode and design Spanner writes to be idempotent.. Exactly-once processing in a Dataflow pipeline requires the pipeline itself to be set to exactly-once mode (which uses consistent snapshots and transactional sinks) and the output writes to Spanner to be idempotent. This combination ensures that even if a record is reprocessed due to failures, the final state in Spanner remains consistent, satisfying compliance requirements.

Answer

Configure Pub/Sub to use exactly-once delivery mode.

Answer

Use Pub/Sub with at-least-once delivery and Dataflow with at-least-once processing mode.

Answer

Enable Dataflow's streaming engine and use Spanner's built-in retry logic.

Question 14

Refer to the exhibit. A data scientist notices that the evaluation component rarely passes the threshold, causing the pipeline to fail often. What should they do to improve efficiency?

Accepted Answer

Add a conditional component that only runs evaluation if training metrics are above a certain level. Adding a conditional component that only runs evaluation when training metrics exceed a certain threshold prevents unnecessary evaluation runs on poorly performing models. This reduces pipeline failures by ensuring that evaluation, which may be resource-intensive or prone to failure with low-quality inputs, is only triggered when the model has demonstrated sufficient training performance. This approach optimizes resource usage and pipeline reliability without sacrificing the evaluation step entirely.

Answer

Reduce the training dataset size

Answer

Remove the evaluation component

Answer

Increase the threshold value

Question 15

A company is building a data lake on Cloud Storage for log analysis. Log files (CSV) arrive every 5 minutes from multiple sources. The files should be ingested into BigQuery for reporting within 15 minutes. Which approach best meets the requirements with minimal operational overhead?

Accepted Answer

Set up a Cloud Storage notification to trigger a Cloud Function that loads each file into BigQuery using the BigQuery API.. Cloud Storage notifications trigger a Cloud Function on each file upload, which then loads the file into BigQuery via the BigQuery API. This provides near-real-time ingestion (within seconds of file arrival) with minimal operational overhead, as there are no servers to manage and no scheduling needed. The 5-minute file arrival and 15-minute SLA are easily met without complex infrastructure.

Answer

Schedule a daily batch load from Cloud Storage to BigQuery using the BigQuery Data Transfer Service.

Answer

Use Dataflow to read from Pub/Sub (ingested from Cloud Storage) and write to BigQuery.

Answer

Use BigQuery federated queries to query the CSV files directly from Cloud Storage.