PCD Practice Exam 59 — 20 Questions

Question 1

A team notices that their application's latency has increased after a recent deployment. They suspect a specific code path is slower. Which Google Cloud tool should they use to identify the most time-consuming functions in their code?

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

Accepted Answer

Cloud Profiler. Cloud Profiler is the correct tool because it performs continuous, low-overhead profiling of CPU and memory usage, pinpointing the specific functions or methods that consume the most resources. Unlike tracing, which focuses on request latency across services, profiling identifies the most time-consuming code paths within a single application process, making it ideal for diagnosing a slow code path after a deployment.

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Cloud Debugger

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Cloud Trace

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Cloud Logging

Question 2

Which TWO capabilities does Cloud Service Mesh (Istio) provide to help monitor application performance? (Select exactly 2.)

Accepted Answer

Automatic generation of HTTP request metrics (e.g., request count, latency, error rate) per service.. Cloud Service Mesh (Istio) automatically generates HTTP request metrics such as request count, latency, and error rate for every service in the mesh. This is achieved through Envoy sidecar proxies that intercept all traffic and export standardized telemetry without requiring any application code changes.

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Legacy Cloud Logging agent integration for container logs.

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Custom Prometheus exporter deployment for each microservice.

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Cloud Endpoints API management with key validation.

Question 3

Which THREE are valid methods for authenticating a user or service when deploying a Cloud Function via the Google Cloud SDK? (Choose 3)

Accepted Answer

Using a user account with 'gcloud auth login'. 'gcloud auth login' authenticates a user account via OAuth 2.0, which is a valid method for deploying Cloud Functions. The Google Cloud SDK uses the user's credentials to authorize API calls, including deployments, making this a standard authentication approach for interactive or user-driven workflows.

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Using an API key

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Using an OAuth 2.0 client ID

Question 4

Refer to the exhibit. You are analyzing application logs and notice that some logs contain a 'trace' field. What does this field enable?

Accepted Answer

It links the log entry to a specific Cloud Trace trace for end-to-end latency analysis. The 'trace' field in a log entry contains the trace ID and span ID that link the log to a specific Cloud Trace trace. This enables end-to-end latency analysis by correlating log entries with the distributed trace that generated them, allowing you to see the full request path across services.

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It is used by Cloud Monitoring to correlate logs with metrics

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It is used to export the log to Cloud Trace

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It indicates that the log was generated by the Cloud Trace agent

Question 5

A team is migrating a monolithic app to microservices. They need to handle distributed transactions across services. Which pattern should they use?

Accepted Answer

Saga pattern. The Saga pattern is the correct choice for managing distributed transactions across microservices because it breaks a long-lived transaction into a sequence of local transactions, each with a compensating action to roll back if a subsequent step fails. This avoids the tight coupling and performance bottlenecks of distributed locking or two-phase commit, which are unsuitable for cloud-native, highly scalable environments. Sagas can be orchestrated (via a coordinator) or choreographed (via events), and they align with eventual consistency principles required for high availability.

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Eventual consistency with compensation

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Distributed lock manager

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Two-phase commit

Question 6

A retail company uses Cloud Spanner as its global transactional database. They need to capture all data changes (inserts, updates, deletes) from specific tables and stream them to a Pub/Sub topic for real-time downstream processing. What Google Cloud feature should they use?

Accepted Answer

Enable Cloud Spanner change streams on the tables and configure them to write to Pub/Sub. Cloud Spanner change streams provide a native, fully managed, and low-latency mechanism to capture row-level changes (inserts, updates, deletes) from Spanner tables. These change streams can be directly configured to write to a Pub/Sub topic via a Dataflow template or a custom pipeline, enabling real-time streaming without polling or external tools.

Answer

Create a Dataflow pipeline that reads from Spanner using JDBC

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Use Cloud Functions to poll the Spanner tables for changes every minute

Answer

Export the Spanner tables to Avro files in GCS and use BigQuery Change Data Capture

Question 7

A company wants to analyse data across Google Cloud and Amazon Web Services without moving the data. They need to query live data in Amazon S3 and Google Cloud Storage from BigQuery. Which feature should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows you to query data across Google Cloud, AWS, and Azure without moving the data. It uses BigQuery's federated query engine to run queries directly on data stored in Amazon S3 and Google Cloud Storage, leveraging BigQuery's standard SQL interface. This meets the requirement of querying live data across both clouds without data movement.

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Cloud Data Fusion

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BigQuery federated queries

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Cloud Dataproc with Spark SQL

Question 8

You need to back up a Cloud Spanner database and store it in a different region for disaster recovery. The backup should be a full database export. What is the recommended method?

Accepted Answer

Use the gcloud command to export the database to Cloud Storage as Avro. The recommended method for backing up a Cloud Spanner database for disaster recovery is to use the `gcloud` command to export the database to Cloud Storage in Avro format. This creates a full database export that can be stored in a different region, enabling restoration in case of a regional failure. Cloud Spanner does not support native backup to other regions; instead, you must export the data to Cloud Storage and then import it into a new instance in the target region.

Answer

Use pg_dump to export Spanner data

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Use Bigtable managed backups

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Use Cloud SQL automated backups

Question 9

You need to continuously replicate change data from an on-premises Oracle database to BigQuery for real-time analytics. Which Google Cloud service is designed specifically for this purpose?

Accepted Answer

Datastream. Datastream is a Google Cloud serverless change data capture (CDC) and replication service specifically designed to continuously replicate data from sources like Oracle databases to BigQuery. It handles schema drift, supports log-based CDC, and provides low-latency streaming, making it the correct choice for real-time analytics.

Answer

Database Migration Service (DMS)

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Cloud Dataproc

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Dataflow

Question 10

A manufacturing company collects sensor data from thousands of devices. They need a database that can handle a write throughput of 100,000 rows per second and read latency under 10ms. Data is keyed by device ID and timestamp. Which Google Cloud database should they choose?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is designed for high-throughput writes and low-latency reads for time-series data. It can handle 100k rows/sec with appropriate node count. Cloud Spanner has lower write throughput per dollar. Cloud SQL is not designed for such high write throughput. Firestore has limits on write throughput per collection.

Answer

Cloud Spanner

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Cloud SQL

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Firestore

Question 11

A developer wants to connect to a Cloud SQL instance from a Compute Engine VM without whitelisting IP addresses and with automatic encryption. What should they use?

Accepted Answer

Use the Cloud SQL Auth Proxy.. Cloud SQL Auth Proxy provides secure, encrypted connections without IP allowlisting. It uses IAM for authentication and runs as a sidecar or on the client. Direct IP with SSL still requires IP allowlisting. Cloud SQL Connector libraries also provide secure connections but the proxy is the standard tool for VMs.

Answer

Use direct IP connection with SSL.

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Use a Cloud SQL Connector library.

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Use a Cloud VPN connection.

Question 12

A company is using Cloud SQL for PostgreSQL and wants to offload read traffic and improve performance. They also want a disaster recovery option in a different region. Which TWO configurations should they implement? (Choose 2.)

Accepted Answer

Create a cross-region read replica in another region.. A cross-region read replica (Option C) allows you to offload read traffic from the primary instance, improving performance by distributing query load. Additionally, because the replica is in a different region, it provides a disaster recovery option: if the primary region fails, you can promote the cross-region replica to a new primary, ensuring business continuity. This directly meets both requirements of offloading reads and providing cross-region DR.

Answer

Configure a same-region read replica.

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Set up pgpool-II for load balancing.

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Enable HA configuration on the primary instance.

Question 13

A developer needs to connect to a Cloud SQL for PostgreSQL instance from a Compute Engine VM without adding the VM's IP to an authorized networks list. Which method should they use?

Accepted Answer

Use Cloud SQL Auth Proxy. The Cloud SQL Auth Proxy is the recommended method for securely connecting to a Cloud SQL instance from a Compute Engine VM without adding the VM's IP to an authorized networks list. It uses mutual TLS (mTLS) to authenticate and encrypt traffic, and it handles IAM-based authorization, so the VM only needs the Cloud SQL Client role and outbound access to the Cloud SQL API (port 443). This avoids exposing the database to the public internet or requiring static IP management.

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Connect via Cloud Shell

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Configure direct IP connection with SSL

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Set up VPC peering and use private IP

Question 14

Your Bigtable instance is experiencing high latency on read queries that scan a large range of rows. The row keys are timestamps in descending order (e.g., '2024-01-01#user123'). What is the most likely cause?

Accepted Answer

Row keys cause hotspotting because of descending timestamps. Descending timestamps as row keys cause hotspotting because new writes (which are always the most recent timestamp) are concentrated on a single tablet server node, creating a 'hot node' that also serves read queries for that range. Bigtable splits and load-balances by row key prefix, so sequential descending keys like '2024-01-01#...', '2024-01-02#...' are written to the same tablet, leading to uneven load and high read latency for scans over large ranges.

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Row keys are too short

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There are too many column families

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Column family design is incorrect

Question 15

A DevOps engineer is designing a row key for a Bigtable table storing user activity logs. The pattern is: user_id (UUID) + timestamp. On a cluster with 10 nodes, they observe severe hotspotting on a single node during peak writes. Which row key design change would likely resolve this issue?

Accepted Answer

Use a hash of the user_id as the prefix. The correct approach to mitigate hotspotting in Bigtable. Using a hash of the user_id as the row key prefix ensures uniform distribution of writes across tablets because the hash function randomizes the prefix, eliminating sequential patterns. Option D (timestamp + user_id) can still cause hotspotting if timestamps are strictly monotonically increasing, as all writes would target the same tablet at any given moment. While timestamps from multiple distributed sources may vary, this design is less robust than hashing and not a guaranteed solution. Therefore, only Option A reliably resolves the hotspotting issue.

Answer

Use a single table with no row key change

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Increase the number of nodes to 20

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Use a composite key: timestamp + user_id

Question 16

A company is migrating a MySQL database to Cloud SQL. They want to test the migration by sending writes to both the old and new databases simultaneously and comparing results. Which TWO techniques should they use?

Accepted Answer

Shadow writes (dual-write to both databases). Shadow writes, also known as dual-writes, allow the application to send the same write operations to both the old MySQL database and the new Cloud SQL database simultaneously. This technique enables real-time comparison of the results from both databases to verify that the migration is handling writes correctly without disrupting the existing production workload.

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Rollback plan documentation

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Load testing with production data shape

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Database Migration Service continuous migration

Question 17

A company is migrating from Oracle to Cloud SQL for PostgreSQL using Database Migration Service. They need to ensure that the migration is validated before cutover. Which TWO actions should they take?

Accepted Answer

Run comparison queries (e.g., row counts, checksums) between source and target.. Running comparison queries (e.g., row counts, checksums) between the source Oracle database and the target Cloud SQL for PostgreSQL instance is a standard validation technique to ensure data consistency and completeness before cutover. Database Migration Service (DMS) handles continuous replication, but manual validation using queries like SELECT COUNT(*) or checksum functions (e.g., MD5 on concatenated columns) catches any discrepancies that might have been introduced during migration or replication lag.

Answer

Take a snapshot of the source database before migration.

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Disable foreign key constraints on the target to speed up migration.

Answer

Implement shadow writes from the application to both databases simultaneously.

Question 18

An organization is migrating an Oracle database to Cloud SQL for PostgreSQL using Database Migration Service. The source has many PL/SQL packages, sequences, and data types (NUMBER, VARCHAR2, DATE, CLOB, RAW). Which FOUR conversions are correct?

Accepted Answer

Map Oracle RAW to PostgreSQL BYTEA.. During migration from Oracle to Cloud SQL for PostgreSQL using DMS, the following mappings are correct: RAW maps to BYTEA (A), NUMBER maps to NUMERIC (C), CLOB maps to TEXT (D), and Oracle packages are converted to PostgreSQL schemas containing functions (E). Option B is incorrect because Oracle DATE includes time components and should map to TIMESTAMP, not DATE.

Answer

Map Oracle DATE to PostgreSQL DATE.

Question 19

A company is designing a microservices application. They want to ensure that if one service fails, it does not cascade to other services. Which pattern should they implement?

Accepted Answer

Circuit Breaker. The Circuit Breaker pattern is the correct choice because it prevents cascading failures by monitoring service calls and opening the circuit when failures exceed a threshold, allowing the system to fail fast and avoid resource exhaustion. This pattern directly addresses the requirement to isolate failures between microservices, ensuring that a failure in one service does not propagate to others.

Answer

Auto-scaling

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Retry with exponential backoff

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Load shedding

Question 20

Your company runs a multi-tier application on Compute Engine with a Cloud SQL backend. Recently, during peak hours, users report slow page loads. Cloud Monitoring shows high CPU on the app servers, but no memory pressure. Cloud Trace shows that the application spends most of its time waiting for database queries. The Cloud SQL instance is a high-memory machine type with 16 vCPUs and 64 GB RAM, but CPU utilization on the database is only 30%. There are no slow query alerts. What is the most likely cause and what should you do?

Accepted Answer

The database lacks indexes. Use Cloud SQL Query Insights to identify missing indexes.. The symptoms—high app server CPU, low database CPU, and queries consuming most of the application’s wait time—point to inefficient queries due to missing indexes. Cloud SQL Query Insights can identify these missing indexes by analyzing query execution plans and wait events. Adding appropriate indexes reduces query execution time, lowering app server CPU usage and resolving the slow page loads. Option A is correct because the database lacks indexes; Query Insights helps find them.

Answer

The application is performing unnecessary queries. Add caching with Memorystore.

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The database connection pool is exhausted. Increase the maximum number of connections.

Answer

The Cloud SQL instance is under-provisioned. Upgrade to a larger machine type.