PDE Practice Test 20 — 15 Questions

Question 1

Match each Google Cloud IAM role to its description.

Question 2

A team deploys a new version of a Cloud Function. After deployment, error rates increase significantly. What is the most efficient way to diagnose the cause?

Accepted Answer

Check Cloud Logging for error stacks and exceptions.. Cloud Logging captures function execution logs and error stacks; reviewing them is the fastest way to understand the failure.

Answer

Deploy a debug version with additional logging.

Answer

Increase the function timeout and retry settings.

Answer

Immediately rollback to the previous version.

Question 3

A Dataflow streaming job is processing high-volume sensor data from thousands of IoT devices. The job uses global windows with a 10-minute processing time trigger. Recently, the job's CPU utilization is nearly 100% and it is falling behind. Which action is most likely to reduce CPU load while maintaining data freshness?

Accepted Answer

Replace GroupByKey with Combine.globally and use a fanout.. Option C is correct because using `Combine.globally` with a fanout reduces the amount of data shuffled and merged in a single worker, lowering CPU load. In Dataflow, `GroupByKey` triggers a full shuffle and per-key aggregation, which is expensive for high-volume sensor data; `Combine.globally` with a fanout performs partial aggregation on each worker before a final merge, reducing network I/O and CPU cycles. This maintains data freshness because the 10-minute processing time trigger still fires on time, but with less per-element overhead.

Answer

Increase the number of workers to distribute the load.

Answer

Change the trigger to event time with a 10-minute allowed lateness.

Answer

Use side inputs to broadcast a static lookup table to all workers.

Question 4

A healthcare company stores patient records as JSON files in Cloud Storage for analysis. They want to design a data lake that enables querying the data with BigQuery while minimizing storage costs and maintaining data security. Which two actions should they take? (Choose two.)

Accepted Answer

Configure object lifecycle management to transition files older than 90 days to Nearline storage.. Correct answers are A and C. Option A is correct because BigLake allows BigQuery to query Cloud Storage data with fine-grained access control and supports various formats. Option C is correct because object lifecycle management can move old data to colder storage classes (e.g., Nearline, Coldline) to reduce costs. Option B is incorrect because encryption is already default; Cloud KMS provides additional control but is not a cost-saving measure. Option D is incorrect because CSV is less efficient for nested data; JSON or Parquet is better. Option E is incorrect because partitioning in BigQuery is for managed tables, not for external tables on Cloud Storage (BigLake supports partitioning but not automatic; however, it's not the primary cost or security action).

Answer

Partition the data by date and store in separate directories for each partition.

Answer

Convert all JSON files to CSV to reduce storage size.

Answer

Enable Cloud KMS to encrypt the data with customer-managed encryption keys.

Question 5

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.. Option B is correct because 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 6

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.. Option A is correct because 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 7

A data science team uses Cloud Build and Vertex AI to implement CI/CD for their machine learning models. Which THREE steps are essential for a production-ready operationalization pipeline? (Choose 3.)

Accepted Answer

Deploy the model to a staging endpoint for manual approval before promoting to production.. Option B is correct because deploying to a staging endpoint for manual approval before promoting to production is a critical step in a production-ready CI/CD pipeline. This allows data scientists to validate model behavior, performance, and fairness in a near-production environment, preventing regressions and ensuring governance compliance before the model serves live traffic.

Answer

Store all training artifacts in Cloud Storage without versioning.

Answer

Automatically deploy every new model version directly to the production endpoint.

Question 8

A company runs a Dataproc cluster for nightly batch jobs. The cluster uses preemptible workers for cost savings. Recently, the jobs have been failing intermittently with 'Disk quota exceeded' errors on the persistent disks attached to the preemptible workers. The cluster is configured with a master node and 10 worker nodes, each with a 100 GB persistent disk. The preemptible workers are dynamically added and removed. What is the most likely cause and the best long-term solution?

Accepted Answer

The preemptible workers are exceeding the project's persistent disk quota in the region because every time a preempted worker restarts, it tries to attach a new disk. Increase the disk quota.. Option C is correct because the intermittent 'Disk quota exceeded' errors on preemptible workers are caused by the project's regional persistent disk quota being exhausted. When a preemptible worker is reclaimed, the cluster attempts to attach a new persistent disk to the replacement worker, but the old disk is not immediately deleted, leading to a buildup of unattached disks that consume quota. The best long-term solution is to increase the persistent disk quota in the region to accommodate the temporary disks from preempted workers.

Answer

The persistent disks of the preemptible workers are too small. Resize the persistent disks to 200 GB each.

Answer

The preemptible workers are using local SSDs that are not recreated on reclaim. Use non-preemptible workers with local SSDs instead.

Answer

The preemptible workers do not have enough persistent disk space to store intermediate shuffle data. Switch to standard workers to avoid this issue.

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.. Option D is correct because 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 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 examining worker logs in Cloud Logging is the first step to identify the root cause of a stuck pipeline. Common issues like out-of-memory errors, serialization failures, or worker crashes are logged there, and without inspecting logs, troubleshooting is guesswork.

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 11

You are designing a streaming Dataflow pipeline that reads from Cloud Pub/Sub. Some data may arrive late due to network delays. You need to ensure that late-arriving data is still processed, but after a certain point, it should be discarded to avoid unbounded state. What is the best practice?

Accepted Answer

Set a watermark and allowed lateness. Option D is correct because in streaming Dataflow pipelines, setting a watermark and allowed lateness provides a mechanism to handle late-arriving data from Pub/Sub without unbounded state growth. The watermark defines the point after which data is considered late, and allowed lateness specifies how long to wait for late data before discarding it, balancing completeness and state management.

Answer

Switch to a batch pipeline

Answer

Use fixed windows without allowed lateness

Answer

Discard all late-arriving data

Question 12

A company is migrating their on-premises Hadoop cluster to Google Cloud. The existing cluster runs HDFS, Hive, and Spark jobs. The migration must minimize changes to existing job code and configuration. The data volume is 50 TB and growing. The team expects to run both batch and interactive SQL queries. Which architecture should they use?

Accepted Answer

Use Cloud Dataproc with ephemeral clusters and Cloud Storage (instead of HDFS) for data storage. Run Spark jobs directly, and use Cloud Dataproc's built-in Hive on Cloud Dataproc for SQL queries.. Option D is correct because it uses Cloud Storage as the underlying storage layer, which is HDFS-compatible and allows existing Spark jobs to run without code changes. Ephemeral Dataproc clusters reduce costs and provide native Hive support for interactive SQL queries, meeting both batch and interactive requirements without altering job configurations.

Answer

Keep HDFS on persistent Cloud Dataproc clusters and use BigQuery for SQL queries.

Answer

Use Cloud Dataflow for all batch processing and BigQuery for storage and querying.

Answer

Migrate HDFS to Cloud Storage, create a Cloud Dataproc cluster for Spark jobs, and use BigQuery for interactive SQL queries via a Hive metastore linked to BigQuery.

Question 13

Your team needs to store time-series data from millions of IoT devices. Each device sends a reading every 5 minutes, and the total data volume is about 2 TB per month. The most common query pattern is retrieving all readings for a specific device over a time range (e.g., last 24 hours). Which storage service should you choose?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for high-throughput, low-latency time-series data. It supports single-row key lookups and range scans, making it ideal for retrieving all readings for a specific device over a time range (e.g., last 24 hours) from millions of IoT devices generating 2 TB/month. Its row key design (e.g., device_id + timestamp) enables efficient time-range queries without full table scans, unlike object storage or analytical warehouses.

Answer

Cloud Storage (objects per device per time interval)

Answer

BigQuery

Answer

Cloud Spanner

Question 14

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 15

Which TWO security best practices should be applied to secure data in transit for a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery? (Choose 2)

Accepted Answer

Enable TLS encryption on all endpoints. Option B is correct because TLS (Transport Layer Security) encryption ensures that data is encrypted during transmission between endpoints, such as between Cloud Pub/Sub and Dataflow workers, and between Dataflow workers and BigQuery. This is a fundamental security best practice for protecting data in transit against eavesdropping and man-in-the-middle attacks.

Answer

Use Cloud Key Management Service (Cloud KMS) to encrypt data in transit

Answer

Use Cloud Armor to protect against DDoS

Answer

Use private IP addresses for Dataflow workers