PDE Practice Test 1 — 10 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