Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 111:

  You are migrating a table to BigQuery and are deeding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID and the city and state in which the store is located You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state city and individual store. You want to model this table to minimize query time and cost. What should you do?

  A. Partition by transaction time; cluster by state first, then city then store ID

  B. Partition by transaction tome cluster by store ID first, then city, then stale

  C. Top-level cluster by stale first, then city then store

  D. Top-level cluster by store ID first, then city then state.

  Correct Answer: C

• Question 112:

  Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

  A. Increase the CPU size on your server.

  B. Increase the size of the Google Persistent Disk on your server.

  C. Increase your network bandwidth from your datacenter to GCP.

  D. Increase your network bandwidth from Compute Engine to Cloud Storage.

  Correct Answer: C

• Question 113:

  Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:

  1.

  The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured

  2.

  Support for publish/subscribe semantics on hundreds of topics Retain per-key ordering

  Which system should you choose?

  A. Apache Kafka

  B. Cloud Storage

  C. Cloud Pub/Sub

  D. Firebase Cloud Messaging

  Correct Answer: A

• Question 114:

  You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. What should you do?

  A. Use Analytics Hub to control data access, and provide third party companies with access to the dataset

  B. Create a Dataflow job that reads the data in frequent time intervals and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

  C. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

  D. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

  Correct Answer: A

  Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. You can use Analytics Hub to create and manage data assets, such as BigQuery datasets, views, and queries, and control who can access them. You can also browse and use data assets that others have shared with you. By using Analytics Hub, you can keep the costs of data sharing low and ensure that the data is current, as the data assets are not copied or moved, but rather referenced from their original sources.

• Question 115:

  You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

  A. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

  B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.

  C. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

  D. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

  Correct Answer: C

• Question 116:

  Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

  A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.

  2. Load the booking and user profile data into a BigQuery table.

  B. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.

  2. Load the booking and user profile data into a BigQuery table.

  C. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.

  2.

  Create a policy tag with the email mask as the data masking rule.

  3.

  Assign the policy to the email field in both tables. A

  4.

  Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

  D. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.

  2.

  Create a policy tag with the default masking value as the data masking rule.

  3.

  Assign the policy to the email field in both tables.

  4.

  Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts

  Correct Answer: B

  Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them. You can then load the de-identified data into a BigQuery table for analysis. References: De-identify sensitive data | Cloud Data Loss Prevention Documentation Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation De-identify and re-identify data with the Cloud DLP API De-identify data in a pipeline

• Question 117:

  You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

  A. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the "ingestion' dataset as the training data.

  B. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

  C. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.

  D. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.

  Correct Answer: D

  Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

• Question 118:

  You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

  A. Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.

  B. Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.

  C. Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

  D. Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.

  Correct Answer: C

  BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:

  1: Introduction to BigLake tables

  2: Deep dive on how BigLake accelerates query performance

  3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)

• Question 119:

  You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.

  

  To do this you need to add a synthetic feature. What should the value of that feature be?

  A. X^2+Y^2

  B. X^2

  C. Y^2

  D. cos(X)

  Correct Answer: D

• Question 120:

  You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

  A. Build Dataform assertions into your code

  B. Write a Spark-based stored procedure.

  C. Build BigQuery user-defined functions (UDFs).

  D. Create Dataplex data quality tasks.

  Correct Answer: A

  Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query. If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases. Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions | Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform