PDE Practice Test 24 — 15 Questions

Question 1

A data engineer wants to create a BigQuery external table that queries data stored in Parquet format in Cloud Storage without loading the data into BigQuery. Which approach is correct?

Accepted Answer

Create an external table definition using a JSON schema file referencing Cloud Storage URIs. Creating an external table definition with a JSON schema file referencing Cloud Storage URIs is a valid direct way to query Parquet data without loading. BigLake (option D) is also a valid approach: BigLake tables are external tables over Cloud Storage that BigQuery can query without loading, with additional governance features. Therefore both B and D are correct. Option A is Cloud SQL, not BigQuery; option C loads data into BigQuery.

Answer

Use Cloud SQL federated query to read Parquet from GCS

Answer

Use the bq load command with --source_format=PARQUET

Question 2

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

Answer

Dataset access controls

Answer

Materialized views

Question 3

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 4

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 5

You train a BigQuery ML linear regression model to predict house prices. The model has high bias during evaluation. Which action BEST reduces bias?

Accepted Answer

Add more features like number of bedrooms and square footage. High bias indicates underfitting, meaning the model is too simple to capture the underlying patterns in the data. Adding more features, such as number of bedrooms and square footage, increases the model's complexity and its ability to fit the training data, thereby reducing bias. This is a standard approach to address underfitting in linear regression.

Answer

Decrease the learning rate in the training options

Answer

Remove features that have low correlation with the label

Answer

Increase L2 regularization

Question 6

Which THREE factors should be considered when designing a Vertex AI Pipeline for continuous training?

Accepted Answer

Cost of training and infrastructure. Cost of training and infrastructure (A) is correct because Vertex AI Pipelines incur compute costs for each pipeline run, including training, data processing, and orchestration. Continuous training amplifies these costs, so you must consider budget constraints, resource optimization (e.g., using preemptible VMs), and cost monitoring to avoid unexpected bills.

Answer

Debugging tools like Cloud Debugger

Answer

Number of model versions to keep

Question 7

A Dataflow streaming job is processing data from Pub/Sub and writing to BigQuery. The job is stuck with the message 'No progress has been made' for several minutes. Which TWO actions should the team take to troubleshoot and resolve the issue? (Choose TWO.)

Accepted Answer

Examine the worker logs in Cloud Logging for any error messages or exceptions.. Option C is correct because when a Dataflow streaming job reports 'No progress has been made,' the first troubleshooting step is to inspect the worker logs in Cloud Logging, which surface exceptions, stuck DoFns, BigQuery write errors, or Pub/Sub backlog issues that explain the stall. Option E is correct because enabling Dataflow Streaming Engine moves pipeline state (such as timers and stateful processing) off the worker VMs and into the Dataflow backend, reducing worker CPU/memory pressure and often resolving stalls caused by state-heavy or resource-constrained workers. Option A is incorrect because updateCompatibility is not a valid Dataflow pipeline option for resolving a stuck job. Option B is incorrect because increasing persistent disk size addresses disk I/O capacity, not the typical causes of a streaming job making no progress. Option D is incorrect because force-stopping and redeploying with --update does not diagnose the root cause and can lose in-flight state without first examining logs.

Answer

Set the updateCompatibility flag to true and restart the pipeline.

Answer

Increase the persistent disk size for all workers to reduce I/O contention.

Answer

Force stop the pipeline and update it with a new version using the --update flag.

Question 8

A data engineer is building a Looker Studio dashboard that requires a calculated field to compute the running total of sales per day per store. Which Looker Studio function should they use?

Accepted Answer

RUNNING_SUM(). Looker Studio's RUNNING_SUM function computes a running total within a group, exactly what is needed for a running total per store partitioned by date.

Answer

RANK()

Answer

TOTAL()

Answer

PERCENTILE()

Question 9

A data engineer is designing a streaming pipeline with Cloud Pub/Sub and Cloud Dataflow. They need to guarantee at-least-once delivery and handle occasional duplicates. Which TWO configurations should they implement?

Accepted Answer

Use idempotent sinks. Idempotent sinks (e.g., BigQuery with insertId, Cloud Storage with object generation numbers) allow the pipeline to safely process duplicate records without causing data corruption or double-counting. In a streaming pipeline with at-least-once semantics, duplicates are inevitable, and idempotent sinks ensure that repeated writes produce the same result as a single write, maintaining data consistency.

Answer

Use global windows with triggers

Answer

Use fixed windows

Answer

Enable Dataflow Streaming Engine

Question 10

Your team uses Looker to develop a model on top of BigQuery. The data is partitioned by ingestion time, and analysts frequently query the last 7 days. However, Looker queries are scanning the entire table, causing high costs. Which change should you implement?

Accepted Answer

Apply a LookML access_filter to dynamically filter on the partition column.. The single best approach is to apply a partition filter requirement in LookML and enable partition pruning in BigQuery. Other options are not directly about Looker or are suboptimal.

Answer

Enable BI Engine on the BigQuery table to accelerate queries.

Answer

Create a materialized view that aggregates data daily and point Looker to that view.

Answer

Use clustering on the order_date column to improve query performance.

Question 11

A data engineer notices that Spark jobs on the Dataproc cluster shown often fail with executor lost errors. What is the most likely reason?

Accepted Answer

All 10 workers are preemptible and can be reclaimed by Compute Engine at any time.. Preemptible VMs in Google Compute Engine can be terminated at any time due to resource contention or other factors, with only 30 seconds notice. If all 10 worker nodes are preemptible, Spark executors running on them will be frequently lost, causing job failures. This is the most direct cause of 'executor lost' errors in a Dataproc cluster.

Answer

The master node has only 4 vCPUs, which may be insufficient for job coordination.

Answer

The cluster is in a single zone, so a zone failure could cause all workers to shut down.

Answer

Autoscaling is enabled and scaling down is causing workers to be removed during job execution.

Question 12

A team needs to store transactional data for an e-commerce application that requires ACID transactions, automatic backups, and point-in-time recovery. The expected workload is under 10,000 QPS. Which database should they choose?

Accepted Answer

Cloud SQL. Cloud SQL is the correct choice because it provides fully managed relational databases (MySQL, PostgreSQL, SQL Server) with built-in ACID transaction support, automated backups, and point-in-time recovery (PITR) via binary logs or write-ahead logs. The workload of under 10,000 QPS is well within Cloud SQL's performance envelope, making it a cost-effective and operationally simple solution for transactional e-commerce data.

Answer

Cloud Bigtable

Answer

Firestore

Answer

Cloud Spanner

Question 13

A company deploys a TensorFlow model on Vertex AI for online predictions. They want to monitor model performance in production to detect degradation. Which TWO practices should they implement? (Choose 2.)

Accepted Answer

Log prediction requests and responses to Cloud Logging and analyze distribution metrics.. Logging prediction requests and responses to Cloud Logging allows you to analyze distribution metrics (e.g., mean, variance, quantiles) over time. This enables detection of data drift or performance degradation by comparing live distributions against baseline distributions, which is a standard monitoring practice for production ML models.

Answer

Use a separate endpoint for shadow testing new model versions.

Answer

Set up Cloud Monitoring alerts for high prediction latency.

Answer

Schedule daily retraining of the model regardless of monitoring alerts.

Question 14

A company uses Dataproc for transient clusters. Which TWO actions can reduce costs?

Accepted Answer

Set cluster autoscaling to minimize idle resources. Dataproc cluster autoscaling automatically adjusts the number of worker nodes based on the YARN memory and CPU utilization metrics. By scaling down during idle periods, you avoid paying for unused compute capacity, directly reducing costs for transient clusters that have variable workloads.

Answer

Increase master node size

Answer

Use standard VMs for all nodes

Answer

Use persistent clusters to avoid creation overhead

Question 15

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