PDE Practice Test 41 — 15 Questions

Question 1

You need to estimate the cost of a BigQuery query before running it. Which command or feature should you use?

Accepted Answer

Use the bq command with the --dry_run flag.. The `bq` command with the `--dry_run` flag allows you to estimate the amount of data a BigQuery query will process before actually executing it. This dry run does not read any data or incur charges; it simply returns the estimated bytes to be processed, which you can use to calculate the cost based on BigQuery's pricing model.

Answer

Check the BigQuery jobs list for similar queries.

Answer

Use the BigQuery cache to estimate if the query is cached.

Answer

Run EXPLAIN on the query to see the query plan.

Question 2

A data analyst wants to compute the rank of sales per region and also the difference in sales between consecutive months for each region. Which BigQuery analytic functions should they use? (Select TWO)

Accepted Answer

RANK(). RANK() (option A) is correct because it assigns a rank to each row within a partition ordered by sales, with tied sales values receiving the same rank and subsequent ranks skipped, which directly satisfies the requirement to compute the rank of sales per region. LAG() (option C) is correct because it accesses the value of sales from the preceding row within the same region partition ordered by month, allowing the analyst to compute the difference in sales between consecutive months. ROW_NUMBER() (option B) is not appropriate here because it assigns unique sequential numbers even for tied sales values, which does not produce a true rank. LEAD() (option D) looks forward to the next row rather than backward, so it would not give the previous month's sales for a consecutive-month difference. NTILE() (option E) divides rows into a specified number of buckets, which is unrelated to ranking sales or computing month-over-month differences.

Answer

ROW_NUMBER()

Answer

LEAD()

Answer

NTILE()

Question 3

You want to monitor the latency of messages in a Pub/Sub subscription. Which Cloud Monitoring metric should you use to see the age of the oldest unacknowledged message?

Accepted Answer

pubsub.googleapis.com/subscription/oldest_unacked_message_age. The Cloud Monitoring metric pubsub.googleapis.com/subscription/oldest_unacked_message_age reports the age (in seconds) of the oldest unacknowledged message in a subscription, which is exactly the latency indicator requested. It is a subscription-level metric that directly reflects how long a message has been waiting without acknowledgment, making it the correct choice for monitoring message age.

Answer

pubsub.googleapis.com/subscription/num_undelivered_messages

Answer

pubsub.googleapis.com/topic/send_request_count

Answer

pubsub.googleapis.com/topic/publish_latency

Question 4

You are building a data pipeline that runs daily batch jobs on Dataproc, then loads results into BigQuery. You want to orchestrate the entire workflow, including dependencies between steps, retries, and monitoring. Which Google Cloud service is most appropriate?

Accepted Answer

Cloud Composer. Cloud Composer is the most appropriate service because it is a fully managed Apache Airflow service that provides workflow orchestration with DAGs, dependency management, retries, scheduling, and monitoring. It natively integrates with Dataproc and BigQuery operators, allowing you to define the entire pipeline as code. This matches the requirement for orchestrating a multi-step daily batch workflow.

Answer

Cloud Scheduler

Answer

Cloud Workflows

Answer

Dataflow

Question 5

You need to process a large volume of event data from Cloud Storage, apply complex transformations using Apache Spark, and then load the results into BigQuery. The data arrives in batches every hour. You want to minimize costs by using preemptible VMs. Which service should you use?

Accepted Answer

Dataproc. Dataproc is a managed Apache Spark and Hadoop service that supports preemptible VMs, making it the ideal choice for batch processing with complex transformations using Spark while minimizing costs. It integrates natively with Cloud Storage for input and BigQuery for output, and preemptible VMs can reduce compute costs by up to 80%. The hourly batch pattern aligns with Dataproc's job-based execution model.

Answer

Cloud Composer

Answer

BigQuery

Answer

Dataflow

Question 6

Refer to the exhibit. A team configured a Cloud Monitoring alerting policy as shown. They recently started receiving false positive alerts. What is the most likely cause?

Accepted Answer

The duration of 60 seconds is too short, making the alert sensitive to brief spikes.. A 60-second duration means the alert fires if the condition is met for just one minute. This is too short to distinguish transient spikes from sustained issues, causing false positives. Increasing the duration would require the metric to breach the threshold for a longer, more meaningful period.

Answer

The alignment period of 60 seconds is too short, causing noise.

Answer

The threshold of 10 is too low.

Answer

The aggregator should be ALIGN_SUM instead of ALIGN_RATE.

Question 7

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 8

A financial company processes transactions in real-time and requires exactly-once processing semantics. They also need to reprocess historical data for backtesting. Which Google Cloud service should they use?

Accepted Answer

Cloud Dataflow. Cloud Dataflow (D) is correct because it provides exactly-once processing semantics via its distributed snapshot mechanism (based on the MillWheel paper) and supports both real-time streaming and batch processing for historical backtesting under a unified programming model. This allows the company to reprocess historical data using the same pipeline code, ensuring consistency across real-time and batch modes.

Answer

Cloud Pub/Sub

Answer

Cloud Functions

Answer

Cloud Dataproc

Question 9

A data scientist developed a model using custom training on Vertex AI. They want to automate the entire training-to-deployment process. Which service should they use?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct choice because it provides a fully managed, serverless orchestration service specifically designed to automate ML workflows, including custom training, hyperparameter tuning, evaluation, and deployment. It integrates natively with Vertex AI services and supports Kubeflow Pipelines SDK or TFX for defining reproducible, end-to-end pipelines, making it the ideal solution for automating the entire training-to-deployment process.

Answer

Cloud Composer

Answer

Cloud Build

Answer

Cloud Functions

Question 10

A financial company needs to process batch trades data daily and ensure that if a transformation step fails, the entire daily run is retried from the beginning. Which design pattern is appropriate?

Accepted Answer

Use an orchestrator like Cloud Composer with retry logic. The requirement states that if any transformation step fails, the entire daily run must be retried from the beginning. An orchestrator like Cloud Composer (Apache Airflow) provides native DAG-level retry logic that can be configured to restart the entire workflow on failure, ensuring atomicity of the batch run. This pattern is essential for maintaining data consistency when partial processing cannot be tolerated.

Answer

Use idempotent writes with checkpointing

Answer

Retry the failed step only

Answer

Use a transactional staging area

Question 11

A team notices that the latency for online predictions from a Vertex AI endpoint has increased significantly over the past hour. The model is a large TensorFlow model deployed with automatic scaling (minReplicaCount=2, maxReplicaCount=10). The CPU utilization of the deployed instances is consistently above 85%. What is the most likely cause of the increased latency?

Accepted Answer

The model is CPU-bound, and the current replicas are saturated, causing queuing.. The consistently high CPU utilization (above 85%) indicates that the existing replicas are saturated, unable to process incoming requests quickly enough. When all replicas are busy, new requests are queued, which directly increases latency. Automatic scaling can add more replicas up to maxReplicaCount=10, but if the scaling is slow or the traffic spike is sudden, queuing occurs first, causing the observed latency increase.

Answer

The network latency between the client and the endpoint has increased due to regional issues.

Answer

The model is deployed with GPU acceleration, but the instances are using incorrect CUDA drivers.

Answer

The model is too large for the instance memory, causing disk swapping.

Question 12

A company has deployed a classification model on Vertex AI. They want to detect data drift in real-time for the model's input features. Which service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to detect data drift and feature skew for models deployed on Vertex AI. It continuously monitors input features against a baseline distribution and alerts when drift exceeds a configured threshold, enabling real-time detection without requiring custom code.

Answer

Cloud Monitoring

Answer

Cloud Data Loss Prevention

Answer

Cloud Logging

Question 13

A data engineer wants to automatically move objects from Standard storage class to Nearline after 30 days, and then to Archive after 365 days. Which Cloud Storage feature should they configure?

Accepted Answer

Object Lifecycle rule with SetStorageClass actions. Object Lifecycle rules in Google Cloud Storage allow you to automatically transition objects between storage classes (e.g., from Standard to Nearline after 30 days, then to Archive after 365 days) using the SetStorageClass action. This feature is specifically designed for automated lifecycle management, including deletion and class transitions, based on object age or other conditions.

Answer

Object Versioning

Answer

Retention Policy

Answer

Bucket Lock

Question 14

A data pipeline using Cloud Pub/Sub and Cloud Dataflow is experiencing duplicate messages. The source system publishes messages at least once. What Dataflow technique ensures exactly-once processing?

Accepted Answer

Use idempotent sinks. Idempotent sinks ensure that even if Cloud Pub/Sub delivers the same message multiple times (due to its at-least-once delivery semantics), the Dataflow pipeline can deduplicate or safely reapply the same data without causing duplicates in the output. This is achieved by designing the sink (e.g., BigQuery with insertId, Cloud Storage with unique filenames) to recognize and ignore repeated writes, effectively providing exactly-once processing semantics downstream.

Answer

Use GlobalWindows

Answer

Set watermark threshold

Answer

Enable streaming engine

Question 15

A company is migrating their on-premises Apache Spark jobs to Dataproc. They want to minimize code changes and take advantage of serverless infrastructure. Which Dataproc feature should they use?

Accepted Answer

Dataproc Serverless Spark. Dataproc Serverless Spark is the correct choice because it allows the company to run Spark workloads without provisioning or managing clusters, minimizing code changes by using the same Spark APIs and libraries. This serverless infrastructure automatically scales resources and handles failures, aligning with the goal of reducing operational overhead while maintaining compatibility with existing Spark jobs.

Answer

Dataproc clusters with preemptible VMs

Answer

Dataproc Workflow Templates

Answer

Dataproc Jobs API with custom machine types