PDE Practice Test 41 — 15 Questions

Question 1

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Bigtable. Cloud Bigtable is the correct choice because it is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, offering consistent sub-10ms latency for high-throughput reads and writes. It natively supports time-series data with row key design optimized for timestamp-based queries, and can handle millions of reads per second across petabytes of data, making it ideal for IoT sensor data.

Answer

BigQuery

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Firestore

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Cloud Spanner

Question 2

Refer to the exhibit. An ML engineer sees this error when invoking a Vertex AI endpoint. What is the most likely cause?

Accepted Answer

The model expects 128x128 images but raw input is 256x256. The error indicates a mismatch between the input dimensions expected by the model and the dimensions of the data being sent to the Vertex AI endpoint. ResNet50 models are commonly trained on 128x128 images, and if the raw input is 256x256, the endpoint will reject the request because the model's input tensor shape does not match. This is a typical input validation error in Vertex AI, where the serving infrastructure checks the shape of the prediction request against the model's signature.

Answer

The input format should be JSON

Answer

The model has a bug in the ResNet50 architecture

Answer

The endpoint is overloaded

Question 3

A company uses Dataflow streaming pipelines to process real-time events. They notice increasing system lag over time. Which two Cloud Monitoring metrics should be examined to diagnose the cause?

Accepted Answer

Dataflow job/system_lag and Dataflow job/data_freshness. System lag measures the time between event ingestion and processing. Data freshness shows the watermark. Worker CPU indicates compute resource issues.

Answer

Pub/Sub subscription/num_undelivered_messages and Dataflow job/watermark_lag

Answer

Dataproc cluster/yarn_allocated_memory_percentage and Dataflow job/worker_cpu

Answer

BigQuery query/execution_times and Dataflow job/elapsed_time

Question 4

Which BigQuery feature allows you to share query results with specific users without giving them direct access to the underlying tables?

Accepted Answer

Authorized views. Authorized views allow sharing results without granting access to the base tables.

Answer

IAM roles

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Dataset access controls

Answer

Materialized views

Question 5

A company wants to use Cloud DLP to inspect data in BigQuery for sensitive information and de-identify it by masking credit card numbers. They want to perform this on a schedule. Which approach should they take?

Accepted Answer

Use Cloud DLP inspect and de-identify jobs triggered by Cloud Scheduler. Cloud DLP can inspect BigQuery tables and de-identify using transforms like masking. Scheduling can be done via Cloud Scheduler.

Answer

Use Dataplex data quality rules with a custom SQL regex

Answer

Use Cloud Data Loss Prevention API with Cloud Composer

Answer

Use BigQuery column-level security with classification

Question 6

A data engineer needs to set up a Dataplex data quality scan to run weekly on a BigQuery table. The scan should check that: (1) the 'email' column is not null, (2) the 'age' column is between 0 and 120, and (3) the 'country_code' column matches a list of valid ISO codes. Which TWO Dataplex features should the engineer use?

Accepted Answer

Schedule the data quality scan using Dataplex scheduling options. The engineer needs to run the scan weekly, so scheduling (A) is required. For the data quality checks, column rules can be used for both the 'age' range check and the 'country_code' set check, as they apply to all values in the column. The 'email' not null check can also be done as a column rule, making a separate row rule unnecessary. Therefore, only A and C are needed.

Answer

Use Cloud DLP to inspect the table for data quality issues

Answer

Create a Dataplex asset and add a tag template to enforce constraints

Answer

Create a row rule for the 'email' column using SQL condition 'email IS NOT NULL'

Question 7

You are designing a Dataflow pipeline that reads from Pub/Sub, performs transformations, and writes to BigQuery. The pipeline must handle schema changes in the incoming data (e.g., new fields appearing). The BigQuery schema should evolve automatically to accept new fields without failing. Which approach should you use?

Accepted Answer

Set the table schema to allow unknown fields and use BigQuery's schema auto-update feature in the pipeline.. Using BigQuery's schema auto-detection combined with specifying the write disposition as WRITE_APPEND and allowing unknown fields can handle schema drift. However, the most robust approach is to use a flexible schema in the pipeline and set BigQuery's schema update options to allow automatic addition of nullable fields.

Answer

Use Dataprep to clean and standardise the data before loading into BigQuery.

Answer

Predefine the BigQuery table schema with all possible fields and use UPDATE to add missing fields.

Answer

Use a JavaScript UDF to parse incoming data and map to a fixed schema, ignoring new fields.

Question 8

You are running a Dataproc cluster for batch processing. The job is not latency-sensitive and you want to minimize cost. You notice that the cluster is underutilized during the job. Which configuration change would reduce costs most effectively?

Accepted Answer

Use preemptible workers for the worker nodes. Using preemptible workers in Dataproc reduces cost by about 60-80% compared to standard VMs. They are suitable for fault-tolerant batch jobs because they can be terminated at any time.

Answer

Resize the cluster to use larger machines

Answer

Switch to single-node cluster

Answer

Enable autoscaling

Question 9

Which Dataflow feature allows you to package a pipeline into a reusable template that can be deployed with different parameters at runtime?

Accepted Answer

Flex Templates. Dataflow Flex Templates allow you to containerize a pipeline and provide runtime parameters, enabling reusability across different environments or jobs.

Answer

Cloud Dataproc

Answer

Classic Templates

Answer

Dataflow SQL

Question 10

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

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topic/send_request_count

Answer

subscription/ack_message_count

Question 11

A BigQuery table has a REQUIRED column 'user_id' that now needs to accept NULL values due to upstream data changes. You want to alter the schema with minimal downtime and no data loss. What should you do?

Accepted Answer

Run `ALTER TABLE dataset.table ALTER COLUMN user_id DROP NOT NULL;`. BigQuery allows changing a column from REQUIRED to NULLABLE using the ALTER TABLE ALTER COLUMN SET DATA TYPE statement. This operation is a metadata change and does not require table recreation or data copy. Dropping and recreating the table would cause downtime and data loss. Using a view is a workaround but doesn't change the underlying schema. Exporting and reloading is disruptive.

Answer

Use the bq command: `bq update --set_nullable_fields user_id dataset.table`

Answer

Create a view that casts user_id to NULLABLE and use the view instead.

Answer

Drop the table and recreate it with the column as NULLABLE.

Question 12

Your Dataflow streaming job is experiencing high system lag. You want to identify the root cause. Which Cloud Monitoring metrics should you examine first? (Choose the best option.)

Accepted Answer

System lag and worker CPU utilization. For streaming Dataflow jobs, system lag measures the maximum time between the event timestamp and when it is processed. High system lag typically indicates that the pipeline cannot keep up with the input rate. Worker CPU utilization is a key metric to check if workers are overloaded. Data freshness is for batch pipelines. Element count alone doesn't show lag. Backlog bytes could be useful but worker CPU is more directly indicative of processing capacity issues.

Answer

Data freshness and worker CPU utilization

Answer

Element count and data freshness

Answer

Backlog bytes and system lag

Question 13

A company is migrating their on-premises Hadoop workloads to Google Cloud. They want to use Dataproc for data processing and need to minimize costs for non-critical batch jobs that can tolerate interruptions. Which TWO configurations should they use?

Accepted Answer

Use preemptible instances for worker nodes. Preemptible instances are cheaper and can be preempted, suitable for fault-tolerant batch jobs. Single-node clusters are cost-effective for small jobs.

Answer

Enable high-availability mode

Answer

Use standard (non-preemptible) instances for all nodes

Answer

Use Dataproc on GKE

Question 14

A company uses Cloud Data Fusion for ETL pipelines. They need to transform sensitive data (PII) by masking certain columns before writing to BigQuery. They also need to ensure the pipeline can be monitored and restarted from failure points. Which THREE features should they use?

Accepted Answer

Enable pipeline monitoring with alerts in Cloud Data Fusion. Data Fusion Wrangler provides a step-by-step recipe for transformations, including masking. Pipeline monitoring and restart from failure are supported by the orchestration framework and checkpointing. Dataproc Metastore is for Hive metadata, not relevant. Data Fusion Studio is the UI, not a feature for monitoring. Cloud Composer is for workflow orchestration, not needed if Data Fusion handles it.

Answer

Use Cloud Composer to schedule and retry the pipeline

Answer

Create a Dataproc Metastore service to store pipeline metadata

Question 15

You are setting up Dataplex data quality rules for a BigQuery table. You want to define rules that check for non-null values in key columns and also validate that a column's values fall within a certain range. Which TWO rule types must you use? (Choose 2)

Accepted Answer

Row rule (e.g., not null). Dataplex data quality rules include row rules (for null checks) and column rules (for range or value checks). Table rules apply to the entire table; custom SQL can be used but row and column rules are the standard.

Answer

Table rule (e.g., row count)

Answer

Partition rule

Answer

Custom SQL rule