PDE Practice Test 19 — 15 Questions

Question 1

A healthcare company must encrypt data in BigQuery with customer-managed keys (CMEK). They want to control the key lifecycle independently. Which approach should they take?

Accepted Answer

Use Cloud KMS to create a key, then set it as the default encryption key for the BigQuery dataset. BigQuery supports CMEK through Cloud KMS. You can create a key ring and key in Cloud KMS, then specify it when creating datasets or tables. The key is used to encrypt data at rest. Column-level encryption is separate and uses AEAD functions with customer-managed keys at the application level.

Answer

Use BigQuery column-level encryption (AEAD) with a key from Cloud KMS

Answer

Enable default encryption on the Cloud Storage bucket used for staging data

Answer

Encrypt the data before loading using a custom application and store the key in Secret Manager

Question 2

An online retailer uses BigQuery for analytics. They have a time-series table with 5 billion rows and new data arrives every day. They want to optimize query performance and reduce costs by ensuring that queries scan only the partitions they need. Which table design should they use?

Accepted Answer

Use a table partitioned on the timestamp column.. Partitioning on the timestamp column allows BigQuery to perform partition pruning, so queries with filters on that column only scan the relevant partitions. This directly reduces the amount of data read, lowering both query cost (pay-per-byte) and improving performance. For a 5-billion-row table with daily data arrival, time-unit partitioning is the standard design to meet the stated goals.

Answer

Use a table clustered on the timestamp column.

Answer

Use a table with no partitioning but use LIMIT in queries.

Answer

Use a table partitioned by ingestion time with a partition expiration.

Question 3

You need to monitor the health of a Pub/Sub subscription that feeds into a Dataflow pipeline. Which TWO Cloud Monitoring metrics are most relevant to detect if messages are not being acknowledged promptly? (Choose 2)

Accepted Answer

subscription/oldest_unacked_message_age. The metric `subscription/num_undelivered_messages` indicates the number of messages not yet delivered/acknowledged. `subscription/oldest_unacked_message_age` shows the age of the oldest unacknowledged message, indicating backlogs.

Answer

subscription/num_outstanding_messages

Answer

topic/send_request_count

Answer

subscription/ack_message_count

Question 4

You need to create a reusable Dataflow pipeline for transforming CSV files in Cloud Storage into Avro files in another bucket. The pipeline should be configurable via runtime parameters (e.g., input and output paths). Which approach should you use?

Accepted Answer

Create a Dataflow Flex Template with a Docker image and parameterized metadata.. Dataflow Flex Templates allow you to package your pipeline code and dependencies into a Docker image, and define parameterized metadata (e.g., input and output paths) that are exposed as runtime parameters. This approach provides full customization of the execution environment and supports reusable, configurable pipelines for transforming CSV to Avro across different Cloud Storage buckets.

Answer

Use Cloud Functions triggered by Cloud Storage events.

Answer

Create a Dataflow Classic Template with the pipeline code and parameters.

Answer

Use Cloud Run Jobs to run the transformation as a container.

Question 5

Refer to the exhibit. A data scientist deploys a model using this configuration. Users report that after a few hours of inactivity, the first prediction request takes over 30 seconds. What is the most likely cause?

Accepted Answer

The automatic scaling configuration allows scaling down to zero replicas, causing a cold start on the first request.. The automatic scaling configuration that allows scaling down to zero replicas means that after a period of inactivity, all model replicas are terminated. When a new prediction request arrives, the endpoint must provision a new replica from scratch, which involves loading the model artifacts, initializing the inference container, and performing health checks. This cold start process typically takes 30 seconds or more, matching the reported behavior.

Answer

The network latency between the client and the endpoint is high due to regional distance.

Answer

The endpoint is misconfigured with the wrong regional endpoint.

Answer

The model is too large and exceeds the instance memory.

Question 6

Which Google Cloud service provides a fully managed, serverless Spark environment without requiring cluster provisioning?

Accepted Answer

Dataproc Serverless. Dataproc Serverless allows running Spark workloads without managing clusters.

Answer

Dataproc on GKE

Answer

Dataflow

Answer

Cloud Data Fusion

Question 7

You are designing a BigQuery data lake for a healthcare organization. The data includes patient records that must be access-controlled at the row level. Which TWO features should you use to meet this requirement?

Accepted Answer

Row-level security using row access policies. Row-level security in BigQuery allows filtering at the row level using access policies. Authorised views can also be used to expose only certain rows. Clustering and materialised views do not provide row-level access control.

Answer

Dataset-level IAM roles

Answer

Clustering on patient_id

Answer

Materialised views

Question 8

A company uses Cloud Pub/Sub to ingest events from multiple sources. They need to guarantee that each event is processed exactly once by downstream consumers. However, Pub/Sub guarantees at-least-once delivery. Which additional steps should they implement to achieve exactly-once processing?

Accepted Answer

Store each message's unique ID in a database and ignore duplicates.. Pub/Sub only provides at-least-once; exactly-once requires consumer-side deduplication using a unique message ID.

Answer

Set the subscription's acknowledgment deadline to 0.

Answer

Enable message deduplication on the subscription.

Answer

Use a dead letter topic to capture duplicates.

Question 9

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 VMs are up to 80% cheaper and can handle job interruptions as Spark is fault-tolerant. Single-node is for testing, not production. High-availability is for long-running clusters with HA requirements. Standard nodes are more expensive.

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 10

Your company uses Pub/Sub to ingest clickstream data. Messages must be processed in order for the same user_id. How should you configure the Pub/Sub subscription to guarantee ordering?

Accepted Answer

Use a pull subscription with enable_message_ordering=true. Pub/Sub ordering keys allow messages with the same key to be delivered in order to subscribers. The subscription must be created with enable_message_ordering set to true.

Answer

Use a pull subscription with exactly-once delivery enabled

Answer

Use a push subscription with acknowledgement deadline set to 600 seconds

Answer

Use a push subscription with a dead letter topic

Question 11

A company wants to design a data pipeline for real-time fraud detection. The system must process streaming financial transactions, enrich them with user profiles from a lookup table, and flag suspicious activities within seconds. Which architecture pattern would be MOST suitable?

Accepted Answer

Kappa architecture using a single stream processing framework like Apache Beam. Kappa architecture uses a single stream processing engine to handle both real-time and batch reprocessing, simplifying the pipeline. Lambda architecture requires maintaining separate batch and streaming layers, increasing complexity. The scenario only requires real-time processing with enrichment, so Kappa is more appropriate.

Answer

Pub/Sub combined with Cloud Functions for stateless processing

Answer

Batch processing with hourly micro-batches using Dataflow

Answer

Lambda architecture with a batch layer for historical analysis and a speed layer for real-time processing

Question 12

A company needs to process high-throughput streaming data with low latency. They are considering Cloud Pub/Sub for ingestion and Cloud Dataflow for processing. However, they are concerned about cost. Which alternative to Cloud Pub/Sub would reduce costs while still meeting the throughput requirements?

Accepted Answer

Cloud Pub/Sub Lite. Pub/Sub Lite is a cost-effective alternative for high-throughput streaming when you don't need global availability or some advanced features of Pub/Sub.

Answer

Cloud Pub/Sub with pull subscriptions

Answer

Cloud Tasks

Answer

Cloud Pub/Sub with push subscriptions

Question 13

A data pipeline uses Cloud Data Fusion to perform ETL jobs. The pipeline reads from BigQuery, transforms data using Wrangler, and writes to Cloud Storage. The team notices that the pipeline runs slower than expected. They suspect the Data Fusion instance is under-provisioned. Which action should be taken to improve performance?

Accepted Answer

Change the Data Fusion instance type from Basic to Enterprise. Cloud Data Fusion uses Dataproc clusters for execution. The instance type (basic, standard, enterprise) determines the Dataproc cluster configuration. Upgrading to a higher edition or increasing the number of worker nodes directly improves throughput. Wrangler transforms are executed on the Dataproc cluster, so more workers help.

Answer

Add more Dataproc Metastore instances

Answer

Enable Data Fusion accelerator for BigQuery

Answer

Rewrite the pipeline using Cloud Dataprep instead

Question 14

You are designing a BigQuery data warehouse for a multi-tenant SaaS application. Each tenant's data must be isolated and queried only by that tenant. You need to minimise management overhead and allow tenants to be added dynamically. Which approach should you use?

Accepted Answer

Use a single dataset with authorized views that filter by tenant_id, granting each tenant access to their view. Authorized views in a shared dataset allow you to create row-level security. By creating a view per tenant that filters by tenant_id, you can grant each tenant access only to its view. This avoids managing multiple datasets and is scalable.

Answer

Use Cloud IAM conditions on the dataset to filter by tenant_id

Answer

Create a separate dataset for each tenant and grant the tenant access to their dataset

Answer

Use a single table with a tenant_id column and enable column-level security to restrict access

Question 15

A Dataproc cluster uses preemptible worker nodes to reduce costs. The cluster runs a long-running Spark job that occasionally experiences worker failures. How should the job be configured to handle preemptible worker failures gracefully?

Accepted Answer

Set spark.task.maxFailures to a high number to allow retries.. Spark jobs should use checkpointing and handle task retries to survive preemption.

Answer

Disable preemptible workers for the job.

Answer

Use persistent disks for preemptible workers.

Answer

Enable automatic restart of the Spark driver on failure.