PDE Practice Exam 1 — 20 Questions

Question 1

Drag and drop the steps to deploy a Cloud Dataflow pipeline from a template into the correct order.

Accepted Answer

Navigate to the Dataflow section of the Cloud Console, then click 'Create Job from Template', then select the desired pipeline template, then specify the required parameters, then click 'Run'.. Deploying a Dataflow pipeline from a template via the Cloud Console involves a specific sequence: first, navigate to the Dataflow page, then create a job from a template, select the appropriate template, fill in the required parameters (such as input/output and temp location), and finally run the job. This order ensures all necessary configurations are applied correctly before execution.

Answer

Select the desired pipeline template, then navigate to the Dataflow section of the Cloud Console, then click 'Create Job from Template', then specify the required parameters, then click 'Run'.

Answer

Navigate to the Dataflow section of the Cloud Console, then specify the required parameters, then select the desired pipeline template, then click 'Create Job from Template', then click 'Run'.

Answer

Navigate to the Dataflow section of the Cloud Console, then click 'Run', then select the desired pipeline template, then specify the required parameters, then click 'Create Job from Template'.

Question 2

A manufacturing company wants to detect anomalies in sensor data from thousands of IoT devices in real time. The data is streaming into Pub/Sub. The best solution should use a machine learning model served from AI Platform that scores sensor readings aggregated over 5-minute windows. Which pipeline design meets these requirements?

Accepted Answer

Use Cloud Dataflow with sliding windows to aggregate sensor readings every 5 minutes, then call a trained model hosted on AI Platform Prediction for each window. Cloud Dataflow's sliding windows natively handle the 5-minute aggregation requirement for streaming data, and its ability to call external services via a DoFn allows integration with AI Platform Prediction for real-time model scoring. This design aligns with the need for low-latency, scalable processing of Pub/Sub streams without managing infrastructure.

Answer

Use Cloud Dataproc with Spark Streaming to aggregate data, and use a Spark ML model embedded in the pipeline

Answer

Use BigQuery streaming inserts and run scheduled queries that call the ML model

Answer

Use Cloud Functions triggered by Pub/Sub to process each sensor reading individually

Question 3

A company runs large batch prediction jobs on Vertex AI every day. They want to minimize costs while ensuring the jobs complete within a 4-hour window. The model requires significant memory. What is the most cost-effective approach?

Accepted Answer

Use preemptible VMs with a machine type that meets memory requirements. Preemptible VMs (now called Spot VMs) are significantly cheaper than standard VMs (up to 60-80% discount) and are ideal for fault-tolerant batch prediction jobs that can handle interruptions. Since the job has a 4-hour window and the model requires significant memory, using preemptible VMs with a machine type that meets the memory requirements minimizes cost while allowing the job to complete if restarted within the time limit.

Answer

Use Cloud TPUs to accelerate predictions

Answer

Use a smaller machine type (e.g., n1-standard-4) to reduce cost

Answer

Use standard VMs and reduce parallelization

Question 4

Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?

Accepted Answer

Save the model using joblib or pickle. Scikit-learn models must be serialized using joblib or pickle to be saved as a model artifact that can be uploaded to Vertex AI. Vertex AI's pre-built prediction containers for scikit-learn expect the model file to be in this format (typically model.joblib or model.pkl) to serve online predictions.

Answer

Write a custom prediction routine

Answer

Containerize the model using Docker

Answer

Create a Vertex AI Endpoint manually

Question 5

A company uses Cloud Dataflow to process streaming data. They notice that the pipeline's throughput is lower than expected and the system is experiencing high latency. What is the most likely cause?

Accepted Answer

Too few workers. In Cloud Dataflow, streaming pipelines require sufficient worker resources to handle the incoming data rate and maintain low latency. When too few workers are provisioned, the pipeline cannot process data quickly enough, leading to increased backlog and higher latency. This is the most likely cause of reduced throughput and high latency in a streaming pipeline.

Answer

Using batch mode instead of streaming mode

Answer

Too many workers

Answer

Incorrect watermark setting

Question 6

You need to allow a data analyst to run queries on a BigQuery dataset but prevent them from modifying the data or deleting the dataset. Which IAM role should you grant?

Accepted Answer

roles/bigquery.dataViewer. roles/bigquery.dataViewer grants read-only access to BigQuery datasets, tables, and views, allowing the analyst to run queries and view metadata but not modify data or delete the dataset. It is the least-privilege role that satisfies the requirement of querying without write or delete permissions. This role is typically paired with roles/bigquery.jobUser at the project level so the user can actually run jobs.

Answer

roles/bigquery.dataOwner

Answer

roles/bigquery.jobUser

Answer

roles/bigquery.dataEditor

Question 7

A team is using BigQuery to analyze petabyte-scale data. They notice that queries are slow and expensive due to full table scans. They have already partitioned by date. What additional optimization should they implement?

Accepted Answer

Cluster by frequently filtered columns. Clustering by frequently filtered columns (option B) organizes data within each partition based on the sort order of those columns. This allows BigQuery to prune blocks during query execution, significantly reducing the amount of data scanned and improving both performance and cost. Since the table is already partitioned by date, clustering adds a secondary ordering that targets the most common filter predicates, avoiding full table scans within each partition.

Answer

Use materialized views

Answer

Convert to native tables

Answer

Use query caching

Question 8

Your company stores sensitive customer data in Cloud Storage. You need to inspect the data for personally identifiable information (PII) and de-identify it before sharing with a third party. Which Google Cloud service should you use?

Accepted Answer

Cloud Data Loss Prevention (DLP). Cloud Data Loss Prevention (DLP) is the correct service because it is specifically designed to inspect, classify, and de-identify sensitive data such as PII in Cloud Storage. It provides built-in infoType detectors for over 150 types of PII and supports de-identification techniques like masking, tokenization, and encryption. This directly matches the requirement to inspect and de-identify data before sharing with a third party.

Answer

Security Command Center

Answer

Dataplex

Answer

Cloud KMS

Question 9

A data scientist wants to test a new model version on a small percentage of traffic before full rollout. Which Vertex AI feature allows this?

Accepted Answer

Endpoint traffic splitting. Vertex AI Endpoint traffic splitting allows you to route a specified percentage of inference requests to different model versions deployed on the same endpoint. This enables gradual rollout by directing a small fraction of traffic (e.g., 5%) to the new model while the rest goes to the current version, without needing separate endpoints or manual routing logic.

Answer

A/B testing

Answer

Model monitoring

Answer

Model versioning with canary deployments

Question 10

A data warehouse team uses Cloud BigQuery for analytics. They want to optimize query performance and reduce costs. Which three actions should they take? (Choose 3)

Accepted Answer

Use partitioned tables on time columns. Partitioning tables on time columns (e.g., DATE, TIMESTAMP) in BigQuery allows the query engine to perform partition pruning, scanning only the relevant partitions instead of the entire table. This directly reduces the amount of data read, lowering query costs and improving performance by limiting I/O to the necessary time range.

Answer

Use automatic reclustering

Answer

Use BI Engine for all queries

Question 11

Your company has a machine learning model that predicts customer churn. The model is deployed on Vertex AI Endpoints with autoscaling. After a marketing campaign, traffic to the endpoint increases by 10x. Some predictions start failing with 'HTTP 503 Service Unavailable' errors. What is the most likely cause?

Accepted Answer

The autoscaling configuration has insufficient maximum nodes to handle the traffic.. A 503 Service Unavailable error from Vertex AI Endpoints indicates that the endpoint is overwhelmed and cannot handle the incoming request volume. With a 10x traffic spike and autoscaling configured, the most likely cause is that the autoscaling configuration has insufficient maximum nodes, so the endpoint cannot scale out enough to handle the load, causing requests to be rejected.

Answer

The model container has a memory leak.

Answer

The model's accuracy has degraded due to data drift.

Answer

The model is using an older version that is not supported.

Question 12

A user gets the above error when trying to get online predictions. The model was created and the endpoint exists. What is the most likely reason?

Accepted Answer

No version of the model is deployed to the endpoint.. The error 'No version of the model is deployed to the endpoint' occurs when the endpoint exists but has no active model version assigned to it. In Google Cloud AI Platform (Vertex AI), an endpoint must have at least one deployed model version to serve predictions. Without a deployed version, the endpoint cannot handle inference requests, even though the endpoint resource exists.

Answer

The endpoint does not exist.

Answer

The endpoint is in a different region than the model.

Answer

The model does not exist.

Question 13

A data science team wants to deploy a model that requires a custom container with specific NVIDIA CUDA version. They build the image and push to Artifact Registry. When deploying to Vertex AI, the model fails to load with an error: 'Failed to start container: invalid ELF header'. What is the most likely cause?

Accepted Answer

The container image was built for a different CPU architecture (e.g., ARM64) than the Vertex AI machine (x86_64). The error 'invalid ELF header' indicates that the container image's executable format is not compatible with the host architecture. Vertex AI uses x86_64 machines, so if the image was built for ARM64 (e.g., on an Apple M1), it will fail to start. This is the most likely cause. Other options would produce different errors (e.g., CUDA incompatibility, permission denied, corrupted model file).

Answer

The model file (saved as .pkl) is corrupted

Answer

The CUDA version in the container is incompatible with the GPU on the machine

Answer

The container does not have the necessary permissions to access the model file in Cloud Storage

Question 14

You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. Some incoming messages are malformed and fail to parse. How should you handle these messages to ensure the pipeline continues processing without data loss?

Accepted Answer

Write malformed messages to a dead-letter sink (e.g., Pub/Sub topic or GCS) and continue processing. Writing malformed messages to a dead-letter sink (Pub/Sub topic or GCS) preserves the data for later inspection while allowing the pipeline to continue processing valid messages. This is the standard Dataflow/Apache Beam pattern for handling unparseable records without halting the stream or silently discarding data. It satisfies both the 'no data loss' and 'continue processing' requirements.

Answer

Configure Pub/Sub to retry indefinitely until the message is processed

Answer

Use a try-catch block in the pipeline and ignore malformed messages

Answer

Set the pipeline to fail and alert the team via Cloud Monitoring

Question 15

In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?

Accepted Answer

It will output the evaluation metrics, and the pipeline can use them for conditional decisions. In Vertex AI Pipelines, a component's YAML definition specifies inputs, outputs, and implementation. Setting `threshold_accuracy` to 0.85 defines a parameter that the component can use internally, but by itself it does not trigger deployment or cause failure. The component's expected behavior is to output evaluation metrics, and the pipeline can then use those metrics in conditional logic (e.g., via `Condition` or `if/else` tasks) to decide subsequent steps, such as model deployment or retraining.

Answer

It will deploy the model if the accuracy meets the threshold

Answer

It will ignore the threshold_accuracy input if not provided

Answer

It will fail if the model accuracy is below 0.85

Question 16

A data engineer needs to automatically delete objects from a Cloud Storage bucket after 30 days and archive them to nearline storage after 7 days. Which configuration should they use?

Accepted Answer

Set a lifecycle rule to SetStorageClass to nearline after 7 days and delete after 30 days. It implements a lifecycle rule that first transitions objects to Nearline storage after 7 days (reducing costs for infrequently accessed data) and then deletes them after 30 days. This matches the requirement to archive after 7 days and delete after 30 days, using the `SetStorageClass` and `Delete` actions in the correct chronological order.

Answer

Set a lifecycle rule to SetStorageClass to nearline after 30 days only

Answer

Set a lifecycle rule to delete objects after 7 days only

Answer

Set a lifecycle rule to delete objects after 7 days and SetStorageClass to nearline after 30 days

Question 17

A data pipeline processes JSON files from Cloud Storage, transforms them using Apache Beam, and writes the output to BigQuery. Some records are malformed and cause the pipeline to fail. How should the engineer handle these errors to ensure the pipeline continues processing while preserving the malformed records for analysis?

Accepted Answer

Catch exceptions in a DoFn and write the malformed records to a separate Cloud Storage bucket using a FileIO sink.. It allows the pipeline to continue processing by catching exceptions within a DoFn and writing malformed records to a separate Cloud Storage bucket using a FileIO sink. This preserves the malformed records for later analysis without blocking the main data flow, which is a standard pattern in Apache Beam for handling dead-letter records. The approach ensures fault tolerance while maintaining data integrity for debugging.

Answer

Set the pipeline to retry malformed records indefinitely until they succeed.

Answer

Use a side input to send malformed records to a dead letter queue in Pub/Sub for later reprocessing.

Answer

Log the malformed records to Stackdriver and skip them in the pipeline.

Question 18

A company wants to use AutoML Tables to build a classification model on a dataset with 100 features and 500,000 rows. They need to deploy the model for online predictions with low latency (<100 ms). Which deployment option should they choose?

Accepted Answer

Deploy the model to an endpoint in Vertex AI using the AutoML endpoint service. AutoML Tables models are natively served through Vertex AI endpoints, which provide a managed, low-latency online prediction service. Deploying to a Vertex AI endpoint using the AutoML endpoint service preserves the AutoML model format and gives the sub-100 ms latency required for online predictions.

Answer

Export the model as a TF SavedModel and deploy on Cloud Run

Answer

Deploy the model on AI Platform Prediction

Answer

Use batch prediction in Vertex AI

Question 19

A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data quality. Which two configuration options should they use? (Choose two.)

Accepted Answer

Use initialization actions for cluster setup.. Option B is correct because initialization actions let you run scripts on every node at cluster creation, so you can install monitoring/validation agents, configure logging, and enforce consistent setup that supports job reliability and data quality across ephemeral clusters. Option E is correct because graceful decommissioning (set via yarn:yarn.resourcemanager.decommissioning.timeout or Dataproc's gracefulDecommissionTimeout) lets running tasks finish before workers are removed, preventing partial job failures and data corruption when nodes are scaled down or preempted. Option A is not appropriate here because preemptible VMs reduce cost but can be reclaimed at any time, which undermines reliability rather than improving it. Option C is not appropriate because idle timeout only deletes idle clusters to save cost; it does not improve job reliability or data quality. Option D is not appropriate because custom machine types tune CPU/memory performance but do not by themselves ensure reliable job completion or data quality.

Answer

Enable preemptible VMs for cost savings.

Answer

Enable idle timeout to automatically delete clusters.

Answer

Use custom machine types for better performance.

Question 20

You are designing a data processing architecture on Google Cloud. You need to ingest data from multiple sources, including streaming events and batch files, and process them to produce a unified dataset for analytics. The solution must support both real-time and historical processing with the same codebase, and be able to handle late-arriving data. Which two Google Cloud services should you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Pub/Sub. To achieve unified batch and stream processing with the same codebase and handle late data, you need a processing engine that supports Apache Beam, such as Cloud Dataflow, and a scalable ingestion service for streaming data, such as Cloud Pub/Sub. Dataflow's Beam model allows you to write one pipeline that works for both batch and streaming, with built-in support for event-time windowing and late data. Pub/Sub provides reliable, scalable ingestion for streaming events.

Answer

Cloud Composer

Answer

Cloud Dataproc

Answer

BigQuery