Brilliant Professional-Data-Engineer Exam Dumps Get Professional-Data-Engineer Dumps PDF [Q44-Q58]

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Brilliant Professional-Data-Engineer Exam Dumps Get Professional-Data-Engineer Dumps PDF

Professional-Data-Engineer Dumps PDF – Professional-Data-Engineer Real Exam Questions Answers

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

NO.44 Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Rewrite the job in Pig.

Rewrite the job in Apache Spark.

Increase the size of the Hadoop cluster.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

NO.45 Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

NO.46 You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

Users are running too many concurrent queries in the system

The [myproject:mydataset.mytable] table has too many partitions

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

NO.47 What Dataflow concept determines when a Window’s contents should be output based on certain criteria being met?

Sessions

OutputCriteria

Windows

Triggers

NO.48 You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don’t get slots to execute their query and you need to correct this. You’d like to avoid introducing new projects to your account.
What should you do?

Convert your batch BQ queries into interactive BQ queries.

Create an additional project to overcome the 2K on-demand per-project quota.

Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

NO.49 After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

NO.50 You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Linear regression

Logistic classification

Recurrent neural network

Feedforward neural network

NO.51 You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

NO.52 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. Reference 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. Reference 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, reference 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. Reference 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.

NO.53 You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

NO.54 You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

NO.55 An online retailer has built their current application on Google App Engine. A new initiative at the company
mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using
a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google
Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

NO.56 You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.

Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.

Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

NO.57 Which of these statements about BigQuery caching is true?

By default, a query’s results are not cached.

BigQuery caches query results for 48 hours.

Query results are cached even if you specify a destination table.

There is no charge for a query that retrieves its results from cache.

NO.58 What are two methods that can be used to denormalize tables in BigQuery?

1) Split table into multiple tables; 2) Use a partitioned table

1) Join tables into one table; 2) Use nested repeated fields

1) Use a partitioned table; 2) Join tables into one table

1) Use nested repeated fields; 2) Use a partitioned table