Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Practice Test 8

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Question 70

A cloud ML change request describes the following issue: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. Which action best addresses the actual requirement?

Use a training-fitted one-hot encoding transform with a defined unseen-category policy.
Encode the ordered tiers using a deliberate ordinal mapping and handle unknown tiers explicitly.
Fit or choose a supported text tokenizer and reuse its saved vocabulary and preprocessing settings for inference.
Implement the join in a correctly partitioned AWS Glue Spark ETL job.

Explanation

Correct Answer

This action fits the situation: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. It implements the needed technical control: Fit or choose a supported text tokenizer and reuse its saved vocabulary and preprocessing settings for inference. The intended outcome is that text inputs are converted into consistent token IDs accepted by the model. The important limitation is that changing tokenization between training and serving can invalidate learned embeddings.

Incorrect Answers

This action is appropriate for another requirement: A small product category field has red, blue, and green values without any intrinsic order, but an engineer currently maps them to 1, 2, and 3. It does not solve the presented constraint: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The matching action for this case is: Fit or choose a supported text tokenizer and reuse its saved vocabulary and preprocessing settings for inference.

This action is appropriate for another requirement: A risk rating has meaningful ordered tiers low, medium, and high, and a product scoring model should retain that order in a compact feature. It does not solve the presented constraint: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The matching action for this case is: Fit or choose a supported text tokenizer and reuse its saved vocabulary and preprocessing settings for inference.

This action is appropriate for another requirement: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. It does not solve the presented constraint: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The matching action for this case is: Fit or choose a supported text tokenizer and reuse its saved vocabulary and preprocessing settings for inference.

Question 71

During a production readiness audit, three independent ML pipeline behaviors must be verified. Workstream A: A churn model uses numerical features with different units, but the team accidentally fits a scaling transform on the complete dataset before holding out validation records. Workstream B: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. Workstream C: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. Select exactly THREE observations, one diagnostic that directly tests each workstream.

Review business-approved age cutoffs and resulting per-bin sample counts.
Inspect the preprocessing pipeline fit calls and compare train-only versus whole-dataset fitted means.
Inspect SageMaker Ground Truth worker instructions and agreement rates on a gold-standard sample.
Inspect tokenizer vocabulary version, special-token mapping, and truncation lengths.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Review AWS Glue DataBrew recipe steps and job output validation reports.

Explanation

Correct Answers

This evidence directly tests the reported behavior: Review business-approved age cutoffs and resulting per-bin sample counts. For this case, a credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. The results tell the team whether to apply the targeted change: Create explicit numeric bins with documented boundaries and a consistent unseen-value policy.

This evidence directly tests the reported behavior: Inspect the preprocessing pipeline fit calls and compare train-only versus whole-dataset fitted means. For this case, a churn model uses numerical features with different units, but the team accidentally fits a scaling transform on the complete dataset before holding out validation records. The results tell the team whether to apply the targeted change: Fit the standardization parameters on training data only and apply the frozen transformation to validation and test splits.

This evidence directly tests the reported behavior: Review AWS Glue DataBrew recipe steps and job output validation reports. For this case, a data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. The results tell the team whether to apply the targeted change: Create and execute a versioned AWS Glue DataBrew recipe on the incoming dataset.

Incorrect Answers

That alternative addresses a different task: A retailer with an existing SageMaker Ground Truth account has thousands of ambiguous product images and needs human labels with periodic consensus verification for supervised classification. The selected workloads instead require Train fitted standardization, Numeric feature binning, Repeatable DataBrew recipe.

That alternative addresses a different task: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The selected workloads instead require Train fitted standardization, Numeric feature binning, Repeatable DataBrew recipe.

That alternative addresses a different task: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. The selected workloads instead require Train fitted standardization, Numeric feature binning, Repeatable DataBrew recipe.

Question 72

During a production readiness audit, three independent ML pipeline behaviors must be verified. Workstream A: A claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. Workstream B: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. Workstream C: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. Select exactly THREE observations, one diagnostic that directly tests each workstream.

Review null prevalence and median-versus-mean differences in the training partition.
Inspect the preprocessing pipeline fit calls and compare train-only versus whole-dataset fitted means.
Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.
Compare raw versus log-transformed feature distributions and inspect nonpositive amounts.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Compare event timestamps and feature retrieval times for leaked post-origination values.

Explanation

Correct Answers

This evidence directly tests the reported behavior: Review null prevalence and median-versus-mean differences in the training partition. For this case, a claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. The results tell the team whether to apply the targeted change: Fit a median imputer to training data and apply it consistently to subsequent data splits.

This evidence directly tests the reported behavior: Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew. For this case, a 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The results tell the team whether to apply the targeted change: Implement the join in a correctly partitioned AWS Glue Spark ETL job.

This evidence directly tests the reported behavior: Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink. For this case, a logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. The results tell the team whether to apply the targeted change: Process the Kinesis stream with an appropriate Managed Service for Apache Flink application.

Incorrect Answers

That alternative addresses a different task: A churn model uses numerical features with different units, but the team accidentally fits a scaling transform on the complete dataset before holding out validation records. The selected workloads instead require Robust missing value imputation, Distributed Glue ETL, Managed streaming transformations.

That alternative addresses a different task: Transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. The selected workloads instead require Robust missing value imputation, Distributed Glue ETL, Managed streaming transformations.

That alternative addresses a different task: A model that predicts defaults at loan origination currently joins the customer's current account balance rather than the balance known on the origination date. The selected workloads instead require Robust missing value imputation, Distributed Glue ETL, Managed streaming transformations.

Question 73

During a production readiness audit, three independent ML pipeline behaviors must be verified. Workstream A: A small product category field has red, blue, and green values without any intrinsic order, but an engineer currently maps them to 1, 2, and 3. Workstream B: Analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. Workstream C: A retailer with an existing SageMaker Ground Truth account has thousands of ambiguous product images and needs human labels with periodic consensus verification for supervised classification. Select exactly THREE observations, one diagnostic that directly tests each workstream.

Compare event timestamps and feature retrieval times for leaked post-origination values.
Review business-approved age cutoffs and resulting per-bin sample counts.
Inspect tokenizer vocabulary version, special-token mapping, and truncation lengths.
Inspect SageMaker Ground Truth worker instructions and agreement rates on a gold-standard sample.
Inspect SageMaker Data Wrangler flow steps and exported processing artifacts.
Inspect how the trained model interprets numeric category levels and the encoded design matrix.

Explanation

Correct Answers

This evidence directly tests the reported behavior: Inspect SageMaker Ground Truth worker instructions and agreement rates on a gold-standard sample. For this case, a retailer with an existing SageMaker Ground Truth account has thousands of ambiguous product images and needs human labels with periodic consensus verification for supervised classification. The results tell the team whether to apply the targeted change: Set up a Ground Truth labeling job with quality controls and appropriate workers.

This evidence directly tests the reported behavior: Inspect SageMaker Data Wrangler flow steps and exported processing artifacts. For this case, analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. The results tell the team whether to apply the targeted change: Create a versioned Data Wrangler flow and export its supported transformations into the ML pipeline.

This evidence directly tests the reported behavior: Inspect how the trained model interprets numeric category levels and the encoded design matrix. For this case, a small product category field has red, blue, and green values without any intrinsic order, but an engineer currently maps them to 1, 2, and 3. The results tell the team whether to apply the targeted change: Use a training-fitted one-hot encoding transform with a defined unseen-category policy.

Incorrect Answers

That alternative addresses a different task: A model that predicts defaults at loan origination currently joins the customer's current account balance rather than the balance known on the origination date. The selected workloads instead require Nominal category one hot encoding, Data Wrangler visual pipeline, Human labeling quality.

That alternative addresses a different task: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. The selected workloads instead require Nominal category one hot encoding, Data Wrangler visual pipeline, Human labeling quality.

That alternative addresses a different task: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The selected workloads instead require Nominal category one hot encoding, Data Wrangler visual pipeline, Human labeling quality.

Question 74

During a production readiness audit, three independent ML pipeline behaviors must be verified. Workstream A: A risk rating has meaningful ordered tiers low, medium, and high, and a product scoring model should retain that order in a compact feature. Workstream B: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. Workstream C: A model that predicts defaults at loan origination currently joins the customer's current account balance rather than the balance known on the origination date. Select exactly THREE observations, one diagnostic that directly tests each workstream.

Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.
Review null prevalence and median-versus-mean differences in the training partition.
Inspect tokenizer vocabulary version, special-token mapping, and truncation lengths.
Inspect rating definitions and encoded values against the documented rank ordering.
Compare event timestamps and feature retrieval times for leaked post-origination values.
Review AWS Glue DataBrew recipe steps and job output validation reports.

Explanation

Correct Answers

This evidence directly tests the reported behavior: Inspect rating definitions and encoded values against the documented rank ordering. For this case, a risk rating has meaningful ordered tiers low, medium, and high, and a product scoring model should retain that order in a compact feature. The results tell the team whether to apply the targeted change: Encode the ordered tiers using a deliberate ordinal mapping and handle unknown tiers explicitly.

This evidence directly tests the reported behavior: Compare event timestamps and feature retrieval times for leaked post-origination values. For this case, a model that predicts defaults at loan origination currently joins the customer's current account balance rather than the balance known on the origination date. The results tell the team whether to apply the targeted change: Perform timestamp-aware historical feature joins using available offline Feature Store records.

Incorrect Answers

That alternative addresses a different task: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The selected workloads instead require Ordinal feature handling, Repeatable DataBrew recipe, Point in time feature retrieval.

That alternative addresses a different task: A claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. The selected workloads instead require Ordinal feature handling, Repeatable DataBrew recipe, Point in time feature retrieval.

That alternative addresses a different task: A service classification model receives free-text customer tickets and must convert words into consistent model input tokens while preserving the same vocabulary during inference. The selected workloads instead require Ordinal feature handling, Repeatable DataBrew recipe, Point in time feature retrieval.

Question 75

During a production readiness audit, three independent ML pipeline behaviors must be verified. Workstream A: Transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. Workstream B: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. Workstream C: A team needs to run a one-off Python preprocessing script using controlled container libraries and write transformed data to S3 without a long-lived notebook. Select exactly THREE observations, one diagnostic that directly tests each workstream.

Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Inspect SageMaker Processing job input/output S3 channels, container image, and execution role.
Compare raw versus log-transformed feature distributions and inspect nonpositive amounts.
Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.
Review AWS Glue DataBrew recipe steps and job output validation reports.
Review null prevalence and median-versus-mean differences in the training partition.

Explanation

Correct Answers

This evidence directly tests the reported behavior: Inspect SageMaker Processing job input/output S3 channels, container image, and execution role. For this case, a team needs to run a one-off Python preprocessing script using controlled container libraries and write transformed data to S3 without a long-lived notebook. The results tell the team whether to apply the targeted change: Run the preprocessing logic as a SageMaker Processing job with versioned code and output paths.

This evidence directly tests the reported behavior: Compare raw versus log-transformed feature distributions and inspect nonpositive amounts. For this case, transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. The results tell the team whether to apply the targeted change: Apply an appropriate log1p-style transform to nonnegative transaction amounts before training.

Incorrect Answers

That alternative addresses a different task: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The selected workloads instead require Heavy tailed feature transform, Managed streaming transformations, SageMaker Processing job isolation.

That alternative addresses a different task: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. The selected workloads instead require Heavy tailed feature transform, Managed streaming transformations, SageMaker Processing job isolation.

That alternative addresses a different task: A claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. The selected workloads instead require Heavy tailed feature transform, Managed streaming transformations, SageMaker Processing job isolation.

Question 76

The ML operations team needs direct evidence about this situation: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. Which diagnostic observation most directly evaluates the reported condition?

Inspect rating definitions and encoded values against the documented rank ordering.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Inspect how the trained model interprets numeric category levels and the encoded design matrix.
Review business-approved age cutoffs and resulting per-bin sample counts.

Explanation

Correct Answer

Incorrect Answers

That diagnostic measures a different AWS ML behavior: A risk rating has meaningful ordered tiers low, medium, and high, and a product scoring model should retain that order in a compact feature. It cannot establish the main condition: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. The decisive evidence for this case is: Review business-approved age cutoffs and resulting per-bin sample counts.

That diagnostic measures a different AWS ML behavior: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. It cannot establish the main condition: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. The decisive evidence for this case is: Review business-approved age cutoffs and resulting per-bin sample counts.

That diagnostic measures a different AWS ML behavior: A small product category field has red, blue, and green values without any intrinsic order, but an engineer currently maps them to 1, 2, and 3. It cannot establish the main condition: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. The decisive evidence for this case is: Review business-approved age cutoffs and resulting per-bin sample counts.

Question 77

A data-science incident must be diagnosed without guessing: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. Which diagnostic observation most directly evaluates the reported condition?

Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Compare raw versus log-transformed feature distributions and inspect nonpositive amounts.
Review business-approved age cutoffs and resulting per-bin sample counts.

Explanation

Correct Answer

Incorrect Answers

That diagnostic measures a different AWS ML behavior: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. It cannot establish the main condition: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The decisive evidence for this case is: Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.

That diagnostic measures a different AWS ML behavior: Transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. It cannot establish the main condition: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The decisive evidence for this case is: Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.

That diagnostic measures a different AWS ML behavior: A credit risk team wants a smooth age measurement grouped into stable policy bands rather than a continuously varying predictor for a regulated report. It cannot establish the main condition: A 4 TB training dataset needs joins across multiple S3 partitions, and a single-node pandas job is exceeding memory. The decisive evidence for this case is: Inspect Glue Spark executor spill metrics, shuffle sizes, and data partition skew.

Question 78

Before making a production change, an engineer must investigate this condition: Analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. Which diagnostic observation most directly evaluates the reported condition?

Review null prevalence and median-versus-mean differences in the training partition.
Compare raw versus log-transformed feature distributions and inspect nonpositive amounts.
Inspect SageMaker Data Wrangler flow steps and exported processing artifacts.
Inspect the preprocessing pipeline fit calls and compare train-only versus whole-dataset fitted means.

Explanation

Correct Answer

Incorrect Answers

That diagnostic measures a different AWS ML behavior: A claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. It cannot establish the main condition: Analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. The decisive evidence for this case is: Inspect SageMaker Data Wrangler flow steps and exported processing artifacts.

That diagnostic measures a different AWS ML behavior: Transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. It cannot establish the main condition: Analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. The decisive evidence for this case is: Inspect SageMaker Data Wrangler flow steps and exported processing artifacts.

That diagnostic measures a different AWS ML behavior: A churn model uses numerical features with different units, but the team accidentally fits a scaling transform on the complete dataset before holding out validation records. It cannot establish the main condition: Analysts need to compare transformations, visualize feature distributions, and export reproducible SageMaker processing code without maintaining a bespoke notebook transformation library. The decisive evidence for this case is: Inspect SageMaker Data Wrangler flow steps and exported processing artifacts.

Question 79

The ML operations team needs direct evidence about this situation: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. Which diagnostic observation most directly evaluates the reported condition?

Inspect SageMaker Ground Truth worker instructions and agreement rates on a gold-standard sample.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.
Inspect how the trained model interprets numeric category levels and the encoded design matrix.
Review AWS Glue DataBrew recipe steps and job output validation reports.

Explanation

Correct Answer

Incorrect Answers

That diagnostic measures a different AWS ML behavior: A retailer with an existing SageMaker Ground Truth account has thousands of ambiguous product images and needs human labels with periodic consensus verification for supervised classification. It cannot establish the main condition: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. The decisive evidence for this case is: Review AWS Glue DataBrew recipe steps and job output validation reports.

That diagnostic measures a different AWS ML behavior: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. It cannot establish the main condition: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. The decisive evidence for this case is: Review AWS Glue DataBrew recipe steps and job output validation reports.

That diagnostic measures a different AWS ML behavior: A small product category field has red, blue, and green values without any intrinsic order, but an engineer currently maps them to 1, 2, and 3. It cannot establish the main condition: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. The decisive evidence for this case is: Review AWS Glue DataBrew recipe steps and job output validation reports.

Question 80

A data-science incident must be diagnosed without guessing: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. Which diagnostic observation most directly evaluates the reported condition?

Compare raw versus log-transformed feature distributions and inspect nonpositive amounts.
Review null prevalence and median-versus-mean differences in the training partition.
Review AWS Glue DataBrew recipe steps and job output validation reports.
Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.

Explanation

Correct Answer

Incorrect Answers

That diagnostic measures a different AWS ML behavior: Transaction dollar amounts span several orders of magnitude, and a linear model is dominated by the largest outliers despite consistent labels. It cannot establish the main condition: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. The decisive evidence for this case is: Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.

That diagnostic measures a different AWS ML behavior: A claims table has numeric repair costs with a long right tail and a few missing entries, making mean replacement highly sensitive to large claims. It cannot establish the main condition: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. The decisive evidence for this case is: Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.

That diagnostic measures a different AWS ML behavior: A data operations analyst cleans inconsistent dates and malformed postal codes in a structured dataset using an auditable sequence of low-code rules. It cannot establish the main condition: A logistics stream requires stateful rolling five-minute counts from Kinesis records without launching and managing an always-on EC2 Spark cluster. The decisive evidence for this case is: Inspect watermark handling, late-arriving records, and application checkpoints in Managed Service for Apache Flink.

Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Dumps, Practice Test Questions

Question 70

Explanation

Correct Answer

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Question 71

Explanation

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Question 72

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Question 73

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Question 74

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Question 75

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Question 76

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Question 77

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Question 78

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Question 79

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Question 80

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