GCP-ADP Data Preparation And Ingestion Practice Test 5 — 15 Questions

Question 1

You need to perform a complex windowing operation on streaming data in Dataflow. Which windowing strategy is best for session-based activity?

Accepted Answer

Session windows. Session windows are designed for session-based activity, where data is grouped by periods of activity separated by gaps of inactivity. They dynamically merge windows based on a specified gap duration, making them ideal for analyzing user sessions. This matches the requirement for complex windowing on streaming data with session-based activity.

Answer

Fixed windows

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Global windows

Answer

Sliding windows

Question 2

You are using BigQuery and need to load data from an external file that is not in GCS. Which method is recommended?

Accepted Answer

Upload to GCS then BigQuery. The recommended method to load external data into BigQuery is to first upload the file to Google Cloud Storage, then use BigQuery's native load job to ingest it. This approach is reliable, scalable, and leverages BigQuery's optimized loading pipeline. It also allows for better error handling and supports various file formats.

Answer

Use Cloud Functions

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Stream directly from the local machine

Answer

Use an external API

Question 3

Which service should you use to ingest streaming data from mobile devices?

Accepted Answer

Pub/Sub. Pub/Sub is designed for real-time messaging and ingestion of streaming data from various sources, including mobile devices. It decouples producers from consumers, allowing mobile apps to publish events to topics that downstream services can subscribe to. This makes it ideal for high-throughput, low-latency streaming ingestion. Other options are storage or analytics services, not ingestion services.

Answer

Cloud Storage

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BigQuery

Answer

Cloud SQL

Question 4

You are using Dataflow with a custom container. Which command do you use to specify the container image?

Accepted Answer

--sdk_container_image. When running an Apache Beam pipeline on Dataflow with a custom container, the pipeline option that specifies the container image is --sdk_container_image. This tells Dataflow to pull the specified image from Artifact Registry or Container Registry and use it as the SDK harness environment for the workers. The other flags do not exist in the Dataflow pipeline options.

Answer

--container_path

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--custom_image

Answer

--worker_image

Question 5

You are using Dataprep to prepare data. How can you share your work with a team?

Accepted Answer

Share the Flow. In Google Cloud Dataprep, a Flow is the primary container that holds all your recipes, datasets, and transformation logic. Sharing the Flow directly with team members grants them access to the entire pipeline, allowing them to view, edit, and run the same data preparation steps. This is the built-in collaboration mechanism designed for team workflows, and it respects IAM permissions on the underlying project and datasets.

Answer

Copy the JSON definition

Answer

Share the underlying GCS file

Answer

Export to a CSV

Question 6

How do you restrict access to a specific GCS bucket to only members of a specific team?

Accepted Answer

Apply an IAM policy to the bucket. Bucket-level IAM policies are the native GCS mechanism for granting access to specific principals. By binding roles such as roles/storage.objectViewer to a Google Group representing the team, only members of that group inherit access to the bucket. This enforces least privilege at the resource boundary without affecting other buckets in the project.

Answer

Use file-level permissions

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Use project-level roles only

Answer

Use public access

Question 7

Which tool is used to monitor the performance of your Dataflow pipelines?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the GCP service that collects and visualizes operational metrics from Dataflow pipelines, including system lag, throughput, worker CPU/memory, and autoscaling behavior. Dataflow natively publishes these metrics to Cloud Monitoring, where you can build dashboards and alerting policies. Cloud Logging captures log entries (errors, debug output) rather than performance metrics, so it is not the monitoring tool.

Answer

Cloud Logging

Answer

BigQuery Console

Answer

Dataprep

Question 8

You are using BigQuery and need to query a large dataset that is partitioned by day. Which clause should you always include for efficiency?

Accepted Answer

WHERE clause on the partition column. BigQuery partitioned tables store data in separate segments by partition column (e.g., DATE or TIMESTAMP). Including a WHERE clause on the partition column allows BigQuery to prune partitions and scan only the relevant data, dramatically reducing bytes processed and cost. Without it, the query scans the entire table, which is inefficient and expensive for large datasets.

Answer

ORDER BY

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LIMIT

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GROUP BY

Question 9

In Dataflow, what is the impact of using a high number of workers for a small dataset?

Accepted Answer

Increased cost and overhead. Dataflow provisions workers as a fixed pool sized by the number of workers you specify, and each worker incurs per-hour Compute Engine charges plus associated overhead (startup, shuffle, network). For a small dataset, the work is finished quickly regardless of parallelism, so extra workers add no throughput benefit while multiplying cost and adding coordination overhead. The correct answer is therefore increased cost and overhead, not faster processing.

Answer

Faster processing

Answer

Decreased latency

Answer

Data corruption

Question 10

You need to perform a rolling update on a Dataflow job without losing current state. How do you do this?

Accepted Answer

Use the Dataflow Update job command. The Dataflow Update job command allows you to update a running pipeline with new code while preserving the pipeline's state, including its data and processing context. This enables rolling updates without losing state.

Answer

Create a new job and point to the same source

Answer

Stop the job and start it again

Answer

Use a pipeline template

Question 11

Which TWO factors should you consider when choosing a GCS storage class?

Accepted Answer

Data retention requirements. Option B (Data retention requirements) is correct because GCS storage classes differ in minimum storage duration and retrieval costs, so how long you must keep data determines whether a class like Nearline, Coldline, or Archive is cost-effective versus Standard. Option E (Access frequency) is correct because storage classes are fundamentally optimized around how often objects are read: Standard for frequent access, Nearline for about once a month, Coldline for about once a quarter, and Archive for less than once a year. Option A (Data color) is not a real GCS concept and has no bearing on storage class selection. Option C (Network speed) affects transfer performance but not which storage class you choose. Option D (Number of users) is irrelevant because GCS storage classes are based on access patterns and retention, not user counts.

Answer

Data color

Answer

Network speed

Answer

Number of users

Question 12

Which THREE of the following are Dataflow windowing types?

Accepted Answer

Fixed. In Google Cloud Dataflow (Apache Beam), the supported windowing types are fixed windows, sliding windows, and session windows, so options A, B, and D are correct. Fixed windows (A) divide a PCollection into non-overlapping, contiguous time intervals of a set duration, such as every 60 seconds. Session windows (B) group elements that arrive close together in time, separated by a gap duration of inactivity, which is useful for user-activity streams. Sliding windows (D) are defined by a window duration and a slide period, allowing windows to overlap, such as a 60-second window that slides every 30 seconds. Binary (C) and Random (E) are not Beam/Dataflow windowing types; no such windowing functions exist in the Dataflow model.

Answer

Binary

Answer

Random

Question 13

Which THREE features does Dataprep provide?

Accepted Answer

Data profiling. Dataprep provides data profiling (A), which automatically analyzes source data to surface schemas, data types, value distributions, and quality issues so users understand their datasets before building recipes. It also provides sampling (B), letting users preview and work with a subset of large datasets to design and test transformations quickly without processing the full volume. Data transformation (E) is a core Dataprep capability, offering a visual, serverless recipe interface (plus Wrangler-style transforms) to clean, standardize, join, and reshape data at scale. Direct database writes (C) are not a Dataprep feature, since Dataprep outputs transformed data to destinations like Cloud Storage or BigQuery rather than writing directly back to source databases. Machine Learning model training (D) is not provided by Dataprep; that belongs to services such as BigQuery ML or Vertex AI, while Dataprep focuses on data preparation.

Answer

Direct database writes

Answer

Machine Learning model training

Question 14

Which TWO of the following are common Dataflow pipeline patterns?

Accepted Answer

Streaming to BigQuery. Option A (Streaming to BigQuery) is correct because a canonical Dataflow pattern is reading unbounded data from a source such as Pub/Sub and writing it into BigQuery for near-real-time analytics, typically using the BigQueryIO connector with streaming inserts or Storage Write API. Option C (Batch processing files from GCS) is correct because another standard Dataflow pattern is batch ingestion of files stored in Cloud Storage, reading them with TextIO/AvroIO/ParquetIO and transforming them before loading to a sink such as BigQuery or GCS. The remaining options are not Dataflow pipeline patterns: B (UI-based data drag-drop) describes a visual ETL tool like Cloud Data Fusion or DataPrep, not Dataflow's Apache Beam programming model; D (Direct database connection) is not a typical Dataflow pattern since Dataflow sources/sinks are Beam I/O connectors rather than direct JDBC-style database connections; and E (Cloud Function execution) is an event-driven serverless compute service, not a Dataflow pipeline pattern.

Answer

UI-based data drag-drop

Answer

Direct database connection

Answer

Cloud Function execution

Question 15

Which TWO of the following are GCP ingestion tools?

Accepted Answer

Cloud Storage. Cloud Storage (A) is correct because it serves as a GCP ingestion tool by allowing data to be loaded into the cloud via gsutil, the JSON/XML APIs, or Storage Transfer Service, and it commonly acts as the landing zone for batch data pipelines feeding BigQuery or Dataproc. Pub/Sub (D) is correct because it is GCP's fully managed, globally scalable messaging service that ingests streaming data from publishers and delivers it to subscribers, making it the primary ingestion mechanism for real-time and event-driven pipelines. Cloud SQL (B) is not an ingestion tool; it is a managed relational database service (MySQL, PostgreSQL, SQL Server) used for storage and querying, not for bringing data into GCP. Cloud Compute (C) is not an ingestion tool; Compute Engine provides IaaS virtual machines, which may run ingestion software but are not themselves an ingestion service. Cloud DNS (E) is not an ingestion tool; it is a scalable, reliable managed DNS service for publishing and resolving domain names.

Answer

Cloud SQL

Answer

Cloud Compute

Answer

Cloud DNS