PDE Practice Test 35 — 15 Questions

Question 1

A team developed a microservice that writes logs to stdout. They want to centralize logs for analysis. Which GCP service should they use to automatically collect and store logs?

Accepted Answer

Use the Cloud Logging client library (google-cloud-logging) for the microservice's language.. The Cloud Logging client library (google-cloud-logging) allows the microservice to write logs directly to Cloud Logging via the Cloud Logging API, without needing a separate agent or intermediate storage. This is the recommended approach for applications running in environments like GKE, Cloud Run, or Compute Engine when you want structured, automatically collected logs that are immediately available for analysis in Cloud Logging.

Answer

Install the Cloud Logging agent on the VM running the microservice.

Answer

Publish logs to a Pub/Sub topic and later store them.

Answer

Write logs directly to Cloud Storage.

Question 2

A company uses Vertex AI Workbench notebooks for data exploration and model development. They want to ensure that the notebook environment can access BigQuery data using the same permissions as the user's Google Cloud account. What is the recommended setup?

Accepted Answer

Create a service account with BigQuery access and attach it to the notebook instance.. Vertex AI Workbench notebooks can use user-managed notebooks with user credentials. By setting the 'User-managed notebook' type and using the 'Add service account' option, you can grant the notebook instance a service account with appropriate BigQuery permissions. Alternatively, you can use the built-in 'Use the same identity as the user' option which uses the user's credentials via OAuth. However, the recommended approach for production is to use a service account for consistent permissions.

Answer

Use a Cloud Functions proxy to authenticate to BigQuery from the notebook.

Answer

Log in to the notebook using the user's Google Cloud credentials via oauth2client.

Answer

Grant the Compute Engine default service account BigQuery access.

Question 3

A company wants to version its ML models and track lineage from training data to deployed model. Which Google Cloud service should they use?

Accepted Answer

Vertex AI ML Metadata. (Vertex AI ML Metadata) is correct because it provides a managed service for tracking model lineage, artifacts, and metadata across the ML lifecycle. Option A (Cloud Storage with object versioning) only stores model files but does not track lineage or relationships. Option B (Data Catalog) is for discovering and managing metadata for data assets, not ML models. Option C (Artifact Registry) is designed for storing container images and build artifacts, not ML model metadata or lineage.

Answer

Cloud Storage with object versioning

Answer

Data Catalog

Answer

Artifact Registry

Question 4

A multinational corporation needs a globally distributed database that supports strong consistency, SQL queries, and automatic failover across regions. They also want to optimize join performance for parent-child relationships. Which TWO features of Cloud Spanner should they use?

Accepted Answer

Strong consistency and automatic failover across regions. Cloud Spanner provides strong consistency and automatic failover across regions as core features. Strong consistency ensures that all reads return the most recent write, which is critical for globally distributed databases that require ACID transactions. Automatic failover across regions is built into Spanner's architecture using synchronous replication and Paxos-based consensus, enabling high availability without manual intervention.

Answer

Secondary indexes

Answer

Bigtable as a caching layer

Answer

Read replicas for global distribution

Question 5

A mobile app uses Firestore to store user profiles. The app allows offline data creation and syncing when connectivity resumes. Which Firestore feature should the developer enable?

Accepted Answer

Enable offline persistence in the Firestore client SDK. Firestore's offline persistence feature allows the client SDK to automatically cache data locally on the device. When the app creates or modifies data while offline, the SDK stores the changes in a local queue and syncs them with the Firestore backend once connectivity is restored. This is the correct and built-in mechanism for offline data creation and syncing.

Answer

Set up a Firestore trigger to cache data in Cloud Memorystore

Answer

Use Cloud Storage signed URLs for offline access

Answer

Enable Firestore multi-region replication

Question 6

A data engineering team uses Cloud Pub/Sub to ingest clickstream events and Cloud Dataflow to process them. They need to maintain strict event ordering per user session, and the processing output must be written to a BigQuery table with exactly-once semantics. Which configuration should the team implement?

Accepted Answer

Enable message ordering in Pub/Sub with a session ID as the ordering key, and in Dataflow use a global window with a custom trigger that fires on watermark and uses a BigQuery sink with 'exactly-once' mode enabled.. It combines Pub/Sub message ordering (using a session ID as the ordering key) with Dataflow's exactly-once sink to BigQuery. The global window with a watermark-based trigger ensures all events for a session are processed in order before writing, while the BigQuery 'exactly-once' mode prevents duplicate rows even if the pipeline retries. This satisfies both strict per-session ordering and exactly-once semantics.

Answer

Use a Pub/Sub pull subscription with a subscriber that acknowledges messages immediately after processing, and a Dataflow pipeline with a sliding window.

Answer

Assign a unique session ID as the message ordering key in Pub/Sub, use a Dataflow pipeline with session windows and .withAllowedLateness(0), and write to BigQuery using a batch load.

Answer

Use a Pub/Sub push subscription with an acknowledgment deadline of 600 seconds and enable exactly-once delivery on the subscription.

Question 7

A team is debugging a sudden increase in prediction latency for a model deployed on Vertex AI Endpoints. Which TWO metrics in Cloud Monitoring should they examine first? (Choose two.)

Accepted Answer

CPU utilization. CPU utilization (A) is correct because a sudden increase in prediction latency often stems from the model consuming excessive CPU cycles during inference, especially for compute-intensive models like deep neural networks. Monitoring CPU utilization helps identify whether the endpoint's compute resources are saturated, causing requests to queue and latency to spike. Memory utilization (B) is correct because insufficient memory can lead to swapping or garbage collection pauses, directly increasing latency. Vertex AI Endpoints autoscales based on these metrics, so examining them first pinpoints resource bottlenecks.

Answer

gRPC port errors

Answer

Number of predictions

Answer

Prediction request latency

Question 8

Drag and drop the steps to set up Cloud IAP (Identity-Aware Proxy) for an App Engine app into the correct order.

Accepted Answer

Enable the Identity-Aware Proxy API, configure the OAuth consent screen, create an OAuth 2.0 client ID, enable IAP for the App Engine application, then add members to the IAP-secured Web App User role.. IAP verifies identity and authorization before allowing access to the application.

Answer

Enable IAP for the App Engine application, enable the Identity-Aware Proxy API, configure the OAuth consent screen, create an OAuth 2.0 client ID, then add members to the IAP-secured Web App User role.

Answer

Configure the OAuth consent screen, create an OAuth 2.0 client ID, enable IAP for the App Engine application, enable the Identity-Aware Proxy API, then add members to the IAP-secured Web App User role.

Answer

Enable the Identity-Aware Proxy API, configure the OAuth consent screen, enable IAP for the App Engine application, create an OAuth 2.0 client ID, then add members to the IAP-secured Web App User role.

Question 9

A company needs to grant analysts access to a BigQuery table that contains sensitive PII columns. The analysts should be able to run aggregate queries on the entire dataset but must not see individual PII values. Which approach should the team use?

Accepted Answer

Use BigQuery column-level security with data masking to mask the PII columns for the analysts' role.. BigQuery column-level security with data masking allows you to define masking policies on specific PII columns (e.g., using `DEFAULT_MASKING_RULE` or custom policies) that automatically transform the data for analysts' roles while still permitting aggregate queries over the entire dataset. This approach ensures analysts never see individual PII values, yet they can run `COUNT`, `SUM`, `AVG`, etc., on the masked columns, meeting both requirements precisely.

Answer

Create a user-defined function (UDF) that aggregates the data and grant analysts permission to call the UDF.

Answer

Use BigQuery row-level security to restrict access to non-PII rows only.

Answer

Create an authorized view that does not include the PII columns and grant analysts access to the view.

Question 10

A company uses BigQuery for real-time analytics. They stream data from IoT devices into a BigQuery table. After a few hours, some of the recent data becomes visible in the table although it was streamed less than 10 minutes ago. The data team confirms that no one ran any manual queries. What is the most likely reason for the data visibility?

Accepted Answer

The streaming buffer reached its maximum capacity (default 90 minutes) and automatically flushed the data to the table.. BigQuery's streaming buffer has a maximum capacity limit, typically around 90 minutes. When the buffer reaches this capacity, BigQuery automatically flushes the buffered data to the table, making it visible. This explains why data streamed less than 10 minutes ago became visible after a few hours.

Answer

The data was stored in the streaming buffer for more than 24 hours, and BigQuery automatically flushes it to the table.

Answer

BigQuery time travel allows querying data from the past, including data still in the streaming buffer.

Answer

The table has an expiration set, and the data is made visible as soon as the table is about to expire.

Question 11

A company is using BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted with a customer-managed key (CMEK). Which approach should they take?

Accepted Answer

Use BigQuery column-level encryption with AEAD functions and a Cloud KMS key.. BigQuery column-level encryption using AEAD (Authenticated Encryption with Associated Data) functions allows you to encrypt specific columns containing PII with a customer-managed key (CMEK) stored in Cloud KMS. This approach provides granular, field-level encryption that meets compliance requirements without affecting the rest of the table or dataset, and the encryption/decryption is performed transparently within BigQuery using the AEAD.DECRYPT_STRING function.

Answer

Use Cloud Data Loss Prevention (DLP) to mask the columns during query.

Answer

Apply CMEK at the dataset level; all tables inherit the encryption.

Answer

Store the data encrypted in Cloud Storage and use external tables with a CMEK.

Question 12

A data engineer needs to design a schema in BigQuery for a dataset that contains customer orders. Each order has a header and multiple line items. Queries frequently need to retrieve the entire order including line items. Which schema design is MOST performant and cost-effective?

Accepted Answer

Use nested and repeated fields (orders table with line items as REPEATED RECORD). BigQuery is optimized for denormalized schemas using nested and repeated fields (REPEATED RECORD). Storing line items as a repeated record within the orders table avoids expensive JOIN operations, reduces data shuffling, and allows BigQuery to scan only the necessary columns, making queries that retrieve entire orders with line items both faster and more cost-effective.

Answer

Store all data in a flat table with repeated order info per line item

Answer

Normalize into separate orders and line_items tables, join on order_id

Answer

Use a partitioned table on order date

Question 13

Match each data pipeline term to its definition.

Accepted Answer

Data Pipeline: A series of steps to move data from sources to destinations.. Common data pipeline concepts: Data Pipeline moves data; ETL transforms before loading; ELT loads then transforms; Data Warehouse stores structured processed data; Data Lake stores raw data. Distractors swap definitions between ETL/ELT and Data Warehouse/Data Lake.

Answer

Data Lake: A central repository for structured, processed data optimized for analysis.

Answer

ETL: Extract, Load, Transform - data is loaded first and then transformed.

Question 14

A company wants to use dbt (data build tool) to transform data in BigQuery. They have a Cloud Storage bucket containing raw CSV files that are loaded daily into BigQuery via an external table. Which dbt feature should they use to modularize the transformation logic and handle dependencies between models?

Accepted Answer

dbt models with ref(). C is correct because dbt models with the `ref()` function allow you to modularize SQL transformation logic and automatically handle dependencies between models. When you use `ref('model_name')`, dbt builds a dependency graph, ensuring models are executed in the correct order based on their references. This is essential for transforming raw data from an external table into a structured, analytics-ready dataset in BigQuery.

Answer

dbt tests

Answer

dbt snapshots

Answer

dbt seeds

Question 15

A team is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?

Accepted Answer

AI Platform Continuous Evaluation. AI Platform Continuous Evaluation (CE) is the correct service because it is specifically designed to monitor deployed models for data drift and feature skew. It automatically compares the distribution of incoming prediction requests against the training data distribution, alerting when statistically significant drift is detected, which directly addresses the need to maintain model quality over time.

Answer

Cloud DLP

Answer

Cloud Monitoring

Answer

Cloud Audit Logs