PDE Practice Test 30 — 15 Questions

Question 1

Which TWO actions can reduce the cost of running a Dataproc cluster for a nightly batch job?

Accepted Answer

Use preemptible VMs for worker nodes.. Option C is correct because preemptible VMs in Dataproc cost significantly less than standard VMs (up to 80% cheaper), and for a nightly batch job that can tolerate interruptions, using preemptible worker nodes reduces compute costs substantially. Option E is correct because Dataproc charges for cluster resources while the cluster exists; deleting the cluster after the nightly job completes ensures you only pay for the duration of the job rather than leaving idle nodes running. Option A is incorrect because increasing worker nodes raises the number of billable VM instances, increasing cost rather than reducing it. Option B is incorrect because high-memory machine types for the master node increase the per-hour price of that node without benefiting a typical batch workload. Option D is incorrect because attaching local SSDs adds cost (and local SSD pricing) to every node, increasing rather than reducing the cluster's expense.

Answer

Increase the number of worker nodes for faster processing.

Answer

Use high-memory machine types for master node.

Answer

Attach local SSDs to all nodes.

Question 2

A gaming company uses Pub/Sub to ingest player events and Dataflow for real-time analytics. They notice that the Pub/Sub subscription backlog is growing despite the Dataflow pipeline running continuously. The pipeline has a 1-hour window for aggregations. What is the most effective way to reduce the backlog?

Accepted Answer

Increase the Dataflow pipeline's worker count via autoscaling.. Increasing the Dataflow pipeline's worker count via autoscaling directly addresses the backlog by adding more parallel processing capacity to consume messages from the Pub/Sub subscription faster. Since the pipeline is continuously running but the backlog grows, the bottleneck is processing throughput, not pipeline availability. Autoscaling allows Dataflow to dynamically allocate more workers based on the backlog size, matching consumption rate to the incoming message rate.

Answer

Use a push subscription instead of pull.

Answer

Decrease the window duration to 10 minutes.

Answer

Enable Pub/Sub topic retention.

Question 3

Which TWO metrics are most important to monitor for a real-time online prediction system to ensure service reliability and model performance?

Accepted Answer

Prediction latency (p50, p99). Prediction latency (p50, p99) is correct because a real-time online prediction system must respond within strict time budgets, and monitoring percentile latencies (especially p99) reveals tail-latency regressions that directly impact service reliability and user experience. Prediction error rate (e.g., 4xx/5xx responses) is correct because it is the primary signal of serving failures, such as malformed requests, timeouts, or model-server crashes, and is essential for detecting availability and correctness problems in production. Feature distribution skew between training and serving, while important for model quality, is a data-quality/drift metric rather than a core real-time reliability metric, so it is not one of the two most important here. The number of training examples used for the latest model version is a training-time artifact and does not reflect live serving health. Batch prediction job throughput applies to offline/batch inference, not to a real-time online prediction system.

Answer

Feature distribution skew between training and serving

Answer

Number of training examples used for the latest model version

Answer

Batch prediction job throughput

Question 4

A company runs a Dataflow streaming pipeline that processes financial transactions. They need to apply a new transformation that enriches the data with a lookup from Cloud Bigtable without stopping the pipeline. The pipeline must be updated in a way that minimises data loss and preserves exactly-once semantics. What is the recommended approach?

Accepted Answer

Use the Dataflow update option with the same pipeline name and new version, ensuring the transform is backward compatible.. Dataflow supports in-place pipeline updates via the update option, which preserves the pipeline's state (including watermarks and deduplication state) and maintains exactly-once semantics. The new transform must be backward compatible with the existing pipeline's state and schema so that the update can be applied without draining.

Answer

Drain the pipeline first, then start a new pipeline with the updated code.

Answer

Create a new pipeline in parallel and switch the Pub/Sub subscription to the new pipeline.

Answer

Stop the pipeline, update the code, and restart with a new pipeline name.

Question 5

A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is designed for custom image classification tasks with minimal ML expertise. It uses transfer learning and supports up to millions of images. AutoML Tables handles tabular data, not images. Vertex AI custom training would require more effort. AutoML NLP is for text data.

Answer

AutoML Tables

Answer

Vertex AI custom training

Answer

AutoML Natural Language

Question 6

A retail company uses BigQuery to store sales data and wants to forecast weekly demand for the next 8 weeks using historical data from the past 2 years. They need to account for seasonality and holidays. Which BigQuery ML model type and configuration is most appropriate?

Accepted Answer

ARIMA_PLUS with holiday_region parameter. ARIMA_PLUS with the holiday_region parameter is the most appropriate BigQuery ML model for forecasting weekly demand with seasonality and holidays. ARIMA_PLUS is specifically designed for time-series forecasting, automatically handles seasonality, trend, and holidays when the holiday_region is specified. It also provides explainability and handles missing data.

Answer

Boosted tree classifier

Answer

Linear regression with engineered time features

Answer

Time-series DECOMPOSE model

Question 7

A company uses Dataproc to run daily Spark ML jobs. The jobs run for 2 hours each day. The team wants to reduce costs without changing job characteristics. Which strategy is MOST cost-effective?

Accepted Answer

Use preemptible instances for worker nodes. Preemptible (Spot) VMs cost up to 80% less than standard VMs and are ideal for fault-tolerant, batch-oriented workloads like Spark ML jobs that can tolerate occasional preemption. Since the jobs run only 2 hours daily and Dataproc automatically handles node replacement when preemptible instances are reclaimed, using preemptible workers delivers the largest cost reduction without changing job characteristics.

Answer

Use a single-node cluster to eliminate overhead

Answer

Enable high-availability mode to avoid restarts

Answer

Increase the number of standard workers to finish faster

Question 8

Drag and drop the steps to set up a BigQuery dataset with a scheduled query into the correct order.

Accepted Answer

Create a BigQuery dataset, then write the SQL query, then configure the scheduled query.. Setting up a BigQuery dataset with a scheduled query involves three logical steps in order: first create a dataset to store your data, then write the SQL query that transforms or loads the data, and finally configure the scheduled query to run at specified intervals. The dataset must exist before you can reference it in the query or schedule, and the query must be defined before you can schedule it. Reversing these steps leads to errors because each step depends on the previous one.

Answer

Write the SQL query, then create the BigQuery dataset, then configure the scheduled query.

Answer

Configure the scheduled query, then create the BigQuery dataset, then write the SQL query.

Answer

Create the BigQuery dataset, then configure the scheduled query, then write the SQL query.

Question 9

A team trained a model on a Vertex AI custom training job and wants to deploy it to an endpoint for online predictions. They have the model artifacts stored in Cloud Storage. What steps are required?

Accepted Answer

Upload model to Model Registry, create endpoint, deploy model. To deploy a model for online predictions on Vertex AI, you must first upload the model artifacts from Cloud Storage to the Model Registry, which creates a versioned model resource. Then you create an endpoint (or use an existing one) and deploy the model to that endpoint, specifying machine type, traffic split, and other settings. This three-step process (upload → create endpoint → deploy) is the required workflow for online serving.

Answer

Directly deploy from Cloud Storage without Model Registry

Answer

Create endpoint, then upload model

Answer

Use Vertex AI Batch Prediction only

Question 10

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 11

A data engineer needs to monitor a Pub/Sub-based streaming pipeline. Which two Cloud Monitoring metrics should be used to detect a backlog of unprocessed messages? (Choose two.)

Accepted Answer

subscription/oldest_unacked_message_age. The 'subscription/num_undelivered_messages' metric shows the number of messages not yet acknowledged, and 'subscription/oldest_unacked_message_age' indicates how long messages have been waiting. Both help detect backlog.

Answer

topic/byte_cost

Answer

topic/send_request_count

Answer

subscription/ack_message_count

Question 12

A gaming company uses Avro schemas for its streaming event data. They anticipate adding new optional fields to events over time. They need to ensure backward compatibility so that existing pipelines continue to work. Which strategy should they adopt?

Accepted Answer

Use Avro with a schema registry that enforces backward-compatible changes. Avro, combined with a schema registry, allows schema evolution with backward compatibility. The registry enforces rules such as adding optional fields with defaults, ensuring that consumers using older schemas can still deserialize new data without breaking. This directly addresses the requirement for existing pipelines to continue working as new optional fields are added.

Answer

Use JSON instead of Avro and ignore unknown fields

Answer

Use Protocol Buffers with breaking changes

Answer

Use FlatBuffers for performance

Question 13

A company stores raw data files in Cloud Storage in a bucket named 'raw-data'. After processing, the files are moved to a 'processed' bucket. To reduce costs, they want to automatically delete raw data older than 30 days. What should they do?

Accepted Answer

Configure a lifecycle rule on the 'raw-data' bucket to delete objects older than 30 days.. Cloud Storage lifecycle management allows you to set a rule that automatically deletes objects after a specified number of days from their creation time. By configuring a lifecycle rule on the 'raw-data' bucket to delete objects older than 30 days, the company can achieve cost reduction without manual intervention. This directly addresses the requirement to remove raw data files that have been processed and are no longer needed.

Answer

Enable object versioning on the 'raw-data' bucket and configure a lifecycle rule to delete noncurrent versions.

Answer

Set a retention policy on the 'raw-data' bucket to expire objects after 30 days.

Answer

Use a bucket policy that denies read access to objects older than 30 days.

Question 14

A data engineer is migrating on-premises Hadoop jobs to Dataproc. Which TWO considerations are important?

Accepted Answer

Use Preemptible VMs for worker nodes to reduce cost. Option A is correct because Dataproc supports preemptible VMs (now called Spot VMs) for worker nodes, which can significantly reduce compute costs for fault-tolerant Hadoop workloads, though they may be reclaimed; Dataproc handles this by replacing preempted workers. Option B is correct because Cloud Storage (GCS) is the recommended storage layer for Dataproc, decoupling storage from compute, allowing clusters to be shut down without data loss, and providing higher durability and scalability than HDFS. Option C is incorrect because the Cloud Storage connector is essential for reading and writing data between Dataproc and GCS, and it does not introduce prohibitive overhead; avoiding it would prevent access to GCS. Option D is incorrect because keeping HDFS ties data to the cluster lifecycle, defeating the benefits of cloud elasticity and durability that GCS provides. Option E is incorrect because while master node availability matters, using on-demand VMs for the master is not a specific migration consideration highlighted here, and Dataproc already uses standard VMs for masters by default; the key cost and storage considerations are preemptible workers and GCS.

Answer

Avoid using Cloud Storage connector to prevent overhead

Answer

Keep HDFS for better performance

Answer

Use on-demand VMs for master node to ensure availability

Question 15

You have a BigQuery table that is used by multiple teams. To save costs, you want to provide a consistent view of the data as of a specific point in time without creating full copies. Which BigQuery feature should you use?

Accepted Answer

Table snapshot. A BigQuery table snapshot creates a lightweight, read-only copy of a table at a specific point in time without duplicating the underlying data, and it is the correct feature for providing a consistent point-in-time view cheaply. Snapshots preserve the table state at creation and can be queried or restored, making them ideal for this requirement.

Answer

Authorized views

Answer

Materialized views

Answer

Table clone