GCP-ADP Practice Test 4 — 10 Questions

Question 1

You are using Vertex AI Workbench. You want to install a new Python library, `seaborn`, for visualization. What is the correct way to install it in the notebook instance?

Accepted Answer

!pip install seaborn. In a Vertex AI Workbench notebook (JupyterLab-based), the correct way to install a Python package is to run a shell command from a notebook cell using the `!` prefix, so `!pip install seaborn` executes pip in the notebook's kernel environment. This installs the library into the instance's Python environment so it can be imported in subsequent cells.

Answer

Use the Google Cloud Console library menu.

Answer

import seaborn

Answer

apt-get install seaborn

Question 2

You need to store sensitive data in GCS. Which feature ensures data is encrypted at rest?

Accepted Answer

Default Encryption. Google Cloud Storage automatically encrypts all data at rest using AES-256 encryption before it is written to disk, with no configuration required. This default encryption ensures that sensitive data is protected without any additional action from the user. While CMEK and Cloud DLP offer additional control or inspection, the fundamental guarantee of encryption at rest is provided by default encryption.

Answer

Identity and Access Management

Answer

Cloud DLP

Answer

Customer-Managed Encryption Keys (CMEK)

Question 3

You need to ensure that PII (Personally Identifiable Information) in BigQuery is automatically detected and tagged across your organization. Which Dataplex feature should you enable on your Lake?

Accepted Answer

Automatic data discovery. Automatic data discovery in Dataplex scans data in a Lake and automatically identifies and tags sensitive information such as PII using built-in or custom data classifiers. It profiles data assets and applies metadata tags, which is exactly what's needed to detect and tag PII across an organization.

Answer

Data quality task

Answer

Lifecycle management policy

Answer

IAM policy inheritance

Question 4

You have a BigQuery table containing millions of rows of web server logs. You need to run a query that filters on a timestamp column and returns only the last 24 hours of data. The table is not partitioned. What should you do to improve query performance and reduce cost for this and future similar queries?

Accepted Answer

Create a partitioned table on the timestamp column and copy the data into it.. Partitioning a large table by a timestamp column enables partition pruning, so queries that filter on that column only scan relevant partitions. This reduces both cost and query time. Without partitioning, even with a WHERE clause, BigQuery scans the entire table. Clustering alone is less effective without partitioning, and materialized views are not ideal for dynamic time windows.

Answer

Create a clustered table on the timestamp column.

Answer

Add a WHERE clause that uses the timestamp column with a specific date range.

Answer

Create a materialized view that selects the last 24 hours of data and refresh it every hour.

Question 5

You need to perform data cleansing on a large dataset in Dataprep. Which execution engine should you choose for scalability?

Accepted Answer

Dataflow. Dataflow is the correct execution engine for Dataprep because Dataprep is built on top of Cloud Dataflow, which provides distributed, autoscaling processing for large datasets. Dataflow handles the parallel execution of Dataprep recipes across many workers, so cleansing operations scale horizontally without manual cluster management. Choosing Dataflow ensures the job can handle terabyte-scale data efficiently.

Answer

Local Desktop

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BigQuery

Answer

Cloud Run

Question 6

You are a data engineer at a healthcare company. You need to create a Dataflow pipeline that reads from a Pub/Sub subscription and writes to BigQuery. The pipeline must handle late-arriving data and ensure exactly-once processing. You also need to minimize cost. Which of the following should you use?

Accepted Answer

Use Dataflow with Apache Beam's PubSubIO and BigQueryIO, enabling windowing and triggers for late data, and use BigQueryIO.Write with exactly-once semantics.. Dataflow with Apache Beam provides the necessary constructs for exactly-once processing and late data handling. PubSubIO and BigQueryIO support windowing and triggers, and BigQueryIO.Write can be configured for exactly-once semantics. Other options either lack these features or are not cost-effective for this use case. Therefore, using Dataflow with these configurations is the correct approach.

Answer

Use Dataflow with the Pub/Sub to BigQuery template and set the --discardUnknownFields option.

Answer

Use BigQuery's streaming ingestion with a Pub/Sub subscription directly.

Answer

Use Cloud Data Fusion with a Realtime pipeline that writes to BigQuery.

Question 7

A marketing analyst needs to run a one-time SQL query that joins a BigQuery table containing campaign performance data with a small CSV file stored in a Cloud Storage bucket. The CSV is updated weekly by an external partner. The analyst wants the query to always reflect the latest CSV without loading it into BigQuery each week. What should the analyst do?

Accepted Answer

Create a BigQuery external table over the CSV file in Cloud Storage and join it with the campaign table in the query.. A BigQuery external table over the Cloud Storage CSV lets queries read the file directly, so the join always sees the latest partner data without any load or refresh job. This is the standard pattern when small reference files change frequently and you want to avoid maintaining a native copy.

Answer

Export the campaign table to Cloud Storage, then run the join in Cloud Shell using a local SQL engine.

Answer

Import the CSV into BigQuery as a native table and set up a scheduled query to re-import it weekly.

Answer

Use the bq load command to append the CSV into a partitioned table each week.

Question 8

You are using Cloud Scheduler to trigger a Workflows execution every hour. The Workflows definition expects an argument 'projectId' to be passed at runtime. You need to ensure the argument is provided correctly. What should you include in the Cloud Scheduler job configuration?

Accepted Answer

In the 'Body' field, provide a JSON payload with the argument, e.g., {"projectId": "my-project"}.. When Cloud Scheduler triggers a Workflows execution, it sends an HTTP POST request to the Workflows API endpoint. Runtime arguments must be included in the request body as a JSON object. The 'Body' field in Cloud Scheduler allows you to specify this JSON payload, ensuring the workflow receives the 'projectId' argument. Other fields like URL, headers, or non-existent attributes do not serve this purpose.

Answer

In the 'URL' field, append the argument as a query parameter, e.g., ?projectId=my-project.

Answer

In the 'Attributes' field, define a key-value pair with 'projectId' as the key and 'my-project' as the value.

Answer

In the 'Headers' field, add a custom header like 'X-Project-Id: my-project'.

Question 9

You are configuring a Dataplex asset that points to a GCS bucket. You want to ensure that only files in a specific folder are included in the asset. How do you specify this?

Accepted Answer

Specify a path filter in the asset definition. In Dataplex, when defining a zone or asset that points to a Cloud Storage bucket, you can specify a path filter (a prefix or folder path) in the asset definition to limit discovery and inclusion to only files under that folder. This is the native mechanism for scoping a Dataplex asset to a subset of a bucket.

Answer

Define a SQL view on the folder

Answer

Use an IAM condition on the bucket

Answer

Move the files to a separate bucket

Question 10

You need to set up an alert when a GCS bucket exceeds a certain size. Which tool do you use?

Accepted Answer

Cloud Monitoring. Cloud Monitoring is the GCP service responsible for collecting metrics, creating alerting policies, and notifying on threshold breaches. GCS automatically exports storage metrics (such as storage.googleapis.com/storage/total_bytes) to Cloud Monitoring, so you can build an alerting policy on bucket size and route it to email, Pub/Sub, or PagerDuty. This is the canonical way to alert on resource metrics in GCP.

Answer

Cloud Build

Answer

IAM

Answer

Cloud Logging