Latest Jan-2025 Google Professional-Data-Engineer Dumps Updated 375 Questions [Q130-Q151]

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Latest Jan-2025 Google Professional-Data-Engineer Dumps Updated 375 Questions

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Google Certified Professional Data Engineer exam is a certification exam offered by Google for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is designed to test candidates on their knowledge of data processing systems, machine learning, and data analysis tools on Google Cloud Platform.

Google Professional-Data-Engineer certification exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual’s ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.

NEW QUESTION 130
You need to choose a database for a new project that has the following requirements:
* Fully managed
* Able to automatically scale up
* Transactionally consistent
* Able to scale up to 6 TB
* Able to be queried using SQL
Which database do you choose?

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore

NEW QUESTION 131
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour.
How should you design the solution?

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

NEW QUESTION 132
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
– No interaction by the user on the site for 1 hour
– Has added more than $30 worth of products to the basket
– Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

NEW QUESTION 133
You currently use a SQL-based tool to visualize your data stored in BigQuery The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness parameter to 4 hours and the enable_refresh parameter to true. Reference: the materialized views in the data visualization tool.

Create views for the visualization queries. Reference: the views in the data visualization tool.

Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference: the materialized views in the data visualization tool.

Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours. Reference: the parquet files in the data visualization tool.

NEW QUESTION 134
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

Publisher throughput quota is too small.

Total outstanding messages exceed the 10-MB maximum.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

The subscriber code does not acknowledge the messages that it pulls.

NEW QUESTION 135
How would you query specific partitions in a BigQuery table?

Use the DAY column in the WHERE clause

Use the EXTRACT(DAY) clause

Use the __PARTITIONTIME pseudo-column in the WHERE clause

Use DATE BETWEEN in the WHERE clause

NEW QUESTION 136
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

NEW QUESTION 137
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

NEW QUESTION 138
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

Redefine the schema by evenly distributing reads and writes across the row space of the table.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

NEW QUESTION 139
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket Has not completed a

transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

NEW QUESTION 140
Which methods can be used to reduce the number of rows processed by BigQuery?

Splitting tables into multiple tables; putting data in partitions

Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause

Putting data in partitions; using the LIMIT clause

Splitting tables into multiple tables; using the LIMIT clause

NEW QUESTION 141
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

NEW QUESTION 142
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Implement clustering in BigQuery on the ingest date column.

Implement clustering in BigQuery on the package-tracking ID column.

Tier older data onto Cloud Storage files, and leverage extended tables.

Re-create the table using data partitioning on the package delivery date.

NEW QUESTION 143
Government regulations in your industry mandate that you have to maintain an auditable record of access
to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store
data that is subject to that mandate?

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be
given to each authorized user.

In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to
provide the auditability.

In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs
will be used to provide the auditability.

In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user
information and logs the access before providing a link to the bucket.

NEW QUESTION 144
Does Dataflow process batch data pipelines or streaming data pipelines?

Only Batch Data Pipelines

Both Batch and Streaming Data Pipelines

Only Streaming Data Pipelines

None of the above

NEW QUESTION 145
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat
a. Which three machine learning applications can you use? (Choose three.)

Supervised learning to determine which transactions are most likely to be fraudulent.

Unsupervised learning to determine which transactions are most likely to be fraudulent.

Clustering to divide the transactions into N categories based on feature similarity.

Supervised learning to predict the location of a transaction.

Reinforcement learning to predict the location of a transaction.

Unsupervised learning to predict the location of a transaction.

NEW QUESTION 146
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.
The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Run a local version of Jupiter on the laptop.

Grant the user access to Google Cloud Shell.

Host a visualization tool on a VM on Google Compute Engine.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

NEW QUESTION 147
Which of the following is not possible using primitive roles?

Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.

Give UserA owner access and UserB editor access for all datasets in a project.

Give a user access to view all datasets in a project, but not run queries on them.

Give GroupA owner access and GroupB editor access for all datasets in a project.

NEW QUESTION 148
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

Place the MariaDB instances in an Instance Group with a Health Check.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Install the StackDriver Agent and configure the MySQL plugin.

NEW QUESTION 149
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

Use Cloud TPUs without any additional adjustment to your code.

Use Cloud TPUs after implementing GPU kernel support for your customs ops.

Use Cloud GPUs after implementing GPU kernel support for your customs ops.

Stay on CPUs, and increase the size of the cluster you’re training your model on.

NEW QUESTION 150
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A sequential numeric ID

A timestamp followed by a stock symbol

A non-sequential numeric ID

A stock symbol followed by a timestamp

NEW QUESTION 151
You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery”s ML. EVALUATE clause without specifying any transformations on the raw input data.

When creating your model, use BigQuery’s TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery’s ML EVALUATE clause without specifying any transformations on the raw input data.

Preprocess all data using Dataflow. At prediction time, use BigOuery”s ML. EVALUATE clause without specifying any further transformations on the input data.

Professional-Data-Engineer Test Engine files, Professional-Data-Engineer Dumps PDF: https://www.testkingit.com/Google/latest-Professional-Data-Engineer-exam-dumps.html

Latest Jan-2025 Google Professional-Data-Engineer Dumps Updated 375 Questions [Q130-Q151]

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