Google Cloud Data engineer free practice test.

This practice test provides targeted preparation for aspiring Data engineers on the Google Cloud platform. It covers essential skills such as identity management and creates and manages robust data processing systems. This includes the ability to design, build, deploy, monitor, maintain, and secure data processing workloads. This resource aids individuals in assessing their readiness and refining their expertise in data engineering practices.

Welcome to your gcpprofessionaldataengineer

When designing your Cloud Bigtable schema which is a recommended design best practice to help avoid slow query performance?

Design row keys for effective querying

Put all data into one column family

Use a date timestamp as the row key

Keep your query definitions short

You're running a Dataflow pipeline that processes streaming data from Pub/Sub and writes the processed data to BigQuery. You notice that due to a bug in the pipeline's code, some of the data written to BigQuery in the last 24 hours is corrupted. How can you recover the corrupted data?

Modify the pipeline code to filter out the corrupted data and reprocess the last 24 hours of data from Pub/Sub.

Use the “Time Travel” feature in BigQuery to restore the table to a state before the corrupted data was ingested.

. Delete the corrupted data from BigQuery and manually reinsert the correct data based on logs stored in Cloud Logging.

Implement a secondary Dataflow pipeline that reads the original data from a backup topic in Pub/Sub, processes it correctly, and writes it to a new BigQuery table.

A solar company is using Dataproc to process a large number of CSV files. The storage option they choose needs to be flexible to serve many worker nodes in multiple clusters. These worker nodes will read the data and also write to it for intermediate storage between processing jobs. What is the recommended storage option on Google Cloud?

Cloud SQL

Local SSD

Cloud Storage

Zonal persistent disks

A global logistics company is looking to optimize its data analytics capabilities by designing a new data model for its burgeoning data lake on GCP. This data lake will centralize diverse datasets, including customer transactions, product inventories, supply chain logistics, and online user interactions. The goal is to enable advanced analytics, machine learning models for demand forecasting, and real-time inventory management. The company is particularly interested in a cost-efficient design that scales with their data growth while ensuring high query performance. Which of the following data model designs for the GCP Data Lake maximizes cost efficiency while maintaining optimal query performance for the company's analytical needs?

Normalized Schema with High Granularity: Design a highly normalized schema that stores data in a granular format across multiple, smaller tables to reduce redundancy. Implement partitioning and clustering on frequently queried columns to improve query performance.

Denormalized Schema for Analytics: Opt for a denormalized schema where transactional data is pre-joined with relevant dimensions like products and customer information. Use partitioning on date columns and clustering on key identifiers to enhance query efficiency.

Hybrid Schema with Materialized Views: Employ a hybrid approach, keeping base transactional data in a normalized form but creating materialized views that denormalize data for common analytical queries. Utilize BigQuery’s materialized views to automatically manage data freshness and reduce compute costs for frequent queries

Schema-on-Read with Nested Structures: Utilize a schema-on-read approach and store data in semi-structured formats (e.g., JSON, Avro) in Cloud Storage, leveraging BigQuery’s capabilities to query nested structures directly. Apply partitioning on ingestion time or event time to manage data scalability.

A Gaming company is deploying a new data analytics platform on GCP and requires a robust data governance framework to manage data staging, cataloging, and discovery. The platform must efficiently handle diverse data types and sources, ensuring data quality and compliance. What architectural approach should be taken to meet these requirements?

Centralize all data in a single BigQuery dataset for simplicity, using custom labels for cataloging and SQL queries for data discovery.

Utilize Cloud Storage for flexible data staging, integrate Data Catalog for dynamic data cataloging, and leverage BigQuery’s metadata for enhanced data discovery.

. Rely exclusively on Cloud Bigtable for data storage, staging, and cataloging, assuming its high throughput will expedite data discovery processes.

Adopt a hybrid approach using Firestore for real-time data staging, Data Catalog for metadata management, and AI Platform for predictive data discovery.

A developer in your team is testing a new application that makes frequent calls to a GCP service's API. They start receiving "rate limit exceeded" errors. What would be the best approach to troubleshoot and resolve this issue? Select two choices.

Reduce the frequency of API calls in the application code to stay within the rate limits.

Request an increase in the API’s rate limits through the GCP Console’s Quotas page

Implement exponential backoff in the application’s error handling to better manage retries after encountering rate limit errors.

Restart the application server to reset the service’s API rate limiter.

A media company is migrating its private data centers to Google Cloud. Over many years, hundreds of terabytes of data were accumulated. They currently have a 100 Mbps line and they need to transfer this data reliably before commencing operations on Google Cloud in 45 days. What should they do?

Store the data in an HTTPS endpoint, and configure Storage Transfer Service to copy the data to Cloud Storage.

Zip and upload the data to Cloud Storage buckets by using the Google Cloud console.

Order a transfer appliance, export the data to it, and ship it to Google.

Upload the data to Cloud Storage by using gsutil.

Your organization's data and applications reside in multiple geographies on Google Cloud. Some regional laws require you to hold your own keys outside of the cloud provider environment, whereas other laws are less restrictive and allow storing keys with the same provider who stores the data. The management of these keys has increased in complexity, and you need a solution that can centrally manage all your keys. What should you do?

Enable confidential computing for all your virtual machines.

Store your keys in Cloud Hardware Security Module (HSM), and retrieve keys from it when required.

Store keys in Cloud Key Management Service (KMS), and reduce the number of days forautomatic key rotation.

Store your keys on a supported external key management partner, and use Cloud External Key Manager (EKM) to get keys when required.

. Business analysts in your team need to run analysis on data that was loaded into BigQuery. You need to follow recommended practices and grant permissions. What role should you grant the business analysts?

bigquery.dataOwner

storage.objectViewer and bigquery.user

bigquery.user and bigquery.dataViewer

bigquery.resourceViewer and bigquery.dataViewer

A Financial institution is migrating its on-premises data to a data warehouse on Google Cloud. This data will be made available to business analysts. Local regulations require that customer information including credit card numbers, phone numbers, and email IDs be captured, but not used in analysis. They need to use a reliable, recommended solution to redact the sensitive data. What should they do?

Create a regular expression to identify and delete patterns that resemble credit card numbers, phone numbers, and email IDs.

Delete all columns with a title similar to “credit card,” “phone,” and “email.”

Use the Cloud Data Loss Prevention (DLP) API to perform date shifting of any entries with credit card numbers, phone numbers, and email IDs.

Use the Cloud Data Loss Prevention (DLP) API to identify and redact data that matches infoTypes like credit card numbers, phone numbers, and email IDs.

You have a Dataflow pipeline that runs data processing jobs. You need to identify the parts of the pipeline code that consume the most resources. What should you do?

Use Cloud Logging

Use Cloud Profiler

Use Cloud Audit Logs

Use Cloud Monitoring

Your company is developing a data pipeline, which will be used as part of a broader ML pipeline for end-to-end machine learning lifecycle management. The data pipeline needs to be able to prepare data, transform data, analyze data, and load data to downstream systems like BigQuery, Cloud SQL. It is required to use Google Cloud for developing and deploying the pipeline. How do you develop the pipeline quickly with a no-code, low-code approach? (Choose Two)

Use Dataprep to develop the pipeline

Use Dataflow to develop the pipeline

Use Dataproc to develop the pipeline

Use Data Fusion to develop the pipeline

Your company has a Big Query dataset named financial records with a table of quarterly earnings. You need to modify the column-level security policy to grant a new group, audit team, access to the net income column, which was previously restricted. Which of the following steps would you take to accomplish this?

Update the dataset’s IAM policy to include the audit_team with roles/bigquery.dataViewer role.

Use the BigQuery ALTER COLUMN statement to add the audit_team to the access policy for the net_income column.

Create a new view including the net_income column and grant the audit_team access to this view.

Remove the column-level security from the net_income column and rely on table-level permissions.

You are deploying a cluster of Compute Engine instances that will process large datasets stored in Cloud Storage. To ensure the system is fault-tolerant and can handle instance failures without interrupting the data processing tasks, what feature or strategy should you implement?

Use managed instance groups with automatic restart and live migration policies configured.

Deploy all instances in a single zone to minimize network latency and simplify management.

Regularly snapshot each instance’s disk and restore them in case of failure.

Manually monitor instance health and replace any failed instances as needed.

A mining company data engineering team receives data in JSON format from external sources at the end of each day. You need to design the data pipeline. What should you do?

Store the data in persistent disks and create an ETL pipeline.

Create a public API to allow external applications to add the data to your warehouse.

Make your BigQuery data warehouse public and ask the external sources to insert thedata.

Store the data in Cloud Storage and create an extract, transform, and load (ETL) pipeline.

You are running Dataflow jobs for data processing. When developers update the code in Cloud Source Repositories, you need to test and deploy the updated code with minimal effort. Which of these would you use to build your continuous integration and delivery (CI/CD) pipeline for data processing?

Cloud Build

Terraform

Compute Engine

Cloud Code

You are creating a data pipeline for streaming data on Dataflow for a e-commerce site point of sales data. You want to calculate the total sales per hour on a continuous basis. Which of these windowing options should you use?

Global window

Hopping windows (sliding windows in Apache Beam)

Session windows

Tumbling windows (fixed windows in Apache Beam)

An ecommerce site is processing large amounts of input data in BigQuery. You need to combine this data with a small amount of frequently changing data that is available in Cloud SQL. What should you do?

Use a federated query to get data from Cloud SQL.

Copy the data from Cloud SQL to a new BigQuery table hourly.

Create a Dataflow pipeline to combine the BigQuery and Cloud SQL data when the Cloud SQL data changes.

Copy the data from Cloud SQL and create a combined, normalized table hourly

As a data manager in your firm you manage a PySpark batch data pipeline by using Dataproc .You want to take a hands-off approach to running the workload, and you do not want to provision and manage your own cluster. What should you do?

Rewrite the job in Spark SQL

Rewrite the job in Dataflow with SQL.

Configure the job to run with Spot VMs.

Configure the job to run on Dataproc Serverless.

You want to build a streaming data analytics pipeline in Google Cloud. You need to choose the right products that support streaming data. Which of these would you choose?

Cloud Storage, Dataprep, AlloyDB

Cloud Storage, Dataflow, Cloud SQL

Pub/Sub, Dataflow, BigQuery

Pub/Sub, Dataprep, BigQuery